Crowd Modeling and Simulation for Safer Building Design

Crowd modeling and simulation are very important in the investigation and study of the dynamics of a crowd. They can be used not only to understand the behavior of a crowd in different environments, but also in risk assessment of spaces and in designing spaces that are safer for crowds, especially during emergency evacuations. This paper provides an overview of the use of the crowd simulation model for three main purposes; (1) as a modeling tool to simulate behavior of a crowd in different environments, (2) as a risk assessment tool to assess the risk posed in the environment, and (3) as an optimization tool to optimize the design of a building or space so as to ensure safer crowd movement and evacuation. Result shows that a simulation using the magnetic force model with a pathfinding feature provides a realistic crowd simulation and the use of ABC optimization can reduce evacuation time and improve evacuation comfort. This paper is expected to provide readers with a clearer idea on how crowd models are used in ensuring safer building planning and design

Modification of physical force approach for simulating agent movement with collective behavior

Crowd modelling is a simulation study to know how crowd will behave in the environment. This simulation will contribute general knowledge and insight especially for safety engineers and architectural designers in assessing safety of crowd movement in buildings. There are many existing crowd models. However, these models neglect the details of agent characteristics and intelligence on how the agent will behave in the real environment. Therefore, in this study, the aim is to present heterogeneous agent characteristics and to include intelligence in the model in order to produce collective types of agent behaviour by modify the existing physical force approach

Development of water surface mobile garbage collector robot

This paper presents a prototype of Water Surface Mobile Garbage Collector Robot built in motivation to educate the people to love and monitor the health of our rivers by collecting the trash themselves using mobile robot. The garbage collector is designed aimed for the cleaning of small-scale lakes, narrow rivers, and drains in Malaysia. The navigation of the robot is controlled using wireless Bluetooth communication from a smartphone application. The performance of the water garbage collector in terms of manoeuvring control efficiency and garbage collection load capacity was tested and evaluated. Based on the experimental results from a swimming pool, it can operate within a 4-metre range and collect 192 grams of small to medium sized recyclable garbage such as food packages, water bottles, and plastics in 10 seconds. It managed to float and navigate on the Panchor River within Bluetooth network range. A strong, lightweight and waterproof material is recommended for use for this water garbage collector. A proximity sensor or image processing technique for detecting garbage on the water surface may be studied and included in the future to enable a fully autonomous manoeuvring control system

Detection of active mobile phone in exam hall

The use of mobile phone as a cheating tool in the examination hall among students have considerably increased a burden to invigilators to ensure integrity in examination hall. Many active mobile phone detection schemes had been proposed as the solution to this problem. However, the detection system function in a small detection range of 1.5 to 2 meters from the detection circuit and does not distinguish various frequency bands of radio frequency signals. In order to have diverse range of RF mobile phone signals detection for alerting the invigilators of their specified monitoring region, antenna is proposed to be used. This is done by antenna design simulation using Computer Simulation Technology (CST) software. Two types of antenna; single-dipole antenna and multi-band dipole antenna are simulated to know the characteristics of VSWR, gain and total efficiency. From the simulation results, multi-band dipole antenna shows acceptable VSWR value which are approximate to 2 V, gain is equal to 2.85 dB and total efficiency is equal to 2.484 dB for 2.4 GHz signal. The results imply positive event that multi-band antenna can be a preferable tool in elaborating accurate RF signal detection of active mobile phone in examination hall

Automotive real-time data acquisition using Wi-Fi connected embedded system

The advancement in embedded systems, which includes the mass deployment of internet-connected electronics, allows the concept of Internet of Things (IoT), to become a reality. This paper discusses one example of how an internet-connected embedded system is utilized in an automotive system. An Electronic Control Unit (ECU), which functions as a control unit in a fuel injection system, are equipped with Wi-Fi capability and installed on 110cc motorcycle. The ECU is connected to multiple sensors that is used by the ECU as part of control system, as well as giving raw data in real time to the server by using Wi-Fi as the communication medium. The server will accumulate data transmitted from ECU by using MQTT protocol, chosen due to its minimal data profile. The data can be visualized through web portal, or opened by any other web-enabled devices. The data collected may also be used later for any other purposes, such as On-Board Diagnostics (OBD) system, etc

Electrical Machine Breakdown Monitoring System Using IoT

Nowadays, electric machines of various types and designs are mainly used in hybrid production systems. Electrical machines are required to control and implement various mechanisms for the production of final products. However, sometimes, for unknown reasons, electrical machines stop working and cause downtime. As a result, this affects work productivity during peak hours and reduces the profitability of the company. To overcome this problem, a proper electrical machine monitoring system is required so that the problem can be addressed and resolved immediately. In this thesis, a prototype of IoT based on the motor breakdown monitoring system using Heltec Lora ESP32 as a microcontroller to communicate with the Blynk IoT platform server is proposed and developed. The parameter values that were monitored are voltage, current and motion sensor reading. The ZMPT101b voltage sensor was used to measure AC voltage while the ACS712 current sensor was used to measure AC current flow through the electrical machine. Moreover, a tilt sensor was used to detect the vibration movement of the motor. The measured values indicate the condition of the motor and were monitored using the developed GUI Blynk IoT platform that can be viewed from mobile phones and website. Email and SMS notifications are sent to users to warn of specified conditions when the measured and uploaded values to the Blynk cloud exceed the configured average values. The effectiveness of the value parameter is analyzed by comparing it with standard measuring device such as clamp meter and multimeter. There were 3 experimental trials conducted and the results showed the lowest average percentage error for voltage sensor was 0.19% and for the current sensor was 17.36%. The tilt sensor on the other hand was not responding to the motor vibrations due to the low sensitivity of the sensor. Overall, all parameter values were successfully displayed on the IoT platform. In the future, the accuracy of the current sensor can be improved by integrating a signal conditioning circuit to filter out noise. Other parameter values suitable for monitoring the motor's status can also be used, such as temperature and water flow sensor

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Improved optimization parameters prediction using the modified mega trend diffusion function for a small dataset problem

This paper proposes a modified mega trend diffusion (MTD) function based on the K-means clustering algorithm to generate artificial samples for a training dataset. This would improve the prediction accuracy in a backpropagation neural network (BPNN) algorithm used in small dataset problems. The main contribution of this paper is in solving the attributes redundancy problem in an mega trend diffusion (MTD) function construction when there are two and three overlapped regions in the functions, using the K-means clustering algorithm. When used in predicting the parameters of an optimization algorithm, significant improvements in the prediction errors were observed, compared to the previous MTD method. The improvements were achieved by clustering the membership function (MF) for each attribute from the overlapped regions in the MF triangle. In this work, this algorithm is used to predict the control parameters of the artificial bee colony optimization (ABCO) (Ni and Li), which was then used in finding the optimal exit door locations of building layouts. For a case study, six samples of multi-room building layouts were considered. Each layout consists of information on the number of rooms (ni), room sizes (si) and corridor width (wi). The performance of the model was evaluated against the conventional MTD method. The superiority of the proposed method over the conventional MTD was confirmed by the 17.67% and 28.68% improvements in the prediction error for twofold and threefold cross-validations, respectively. It is envisaged that the method can be very useful in improving the prediction error of data samples of various scales and with different sizes of artificial data

Crowd Modelling Validation for Modified Social Force Model

Crowd modeling is mainly used to observe and analyze the movement pattern of the crowd, including their behavior with the influence of building geometry. It also has been used widely in many application areas such as for transportation services, urban planning and event planning. Representation of crowd dynamics using a simulation tool is useful in various crowd studies, where experiments with humans are too dangerous and not practical to be implemented. As to ensure the validity and accuracy of the developed simulation model, it has to be validated with the real data, in which most of recent crowd modeling works are lacking. Therefore, in this paper, we propose three types of approaches to validate our proposed crowd simulation model, the Magnetic Social Force Model, which are the component testing, qualitative validation and quantitative validation. Real data of crowd movement at concourse area of a train station in Kuala Lumpur has been used for the validation purpose in this work. By comparing the simulation analysis with the real data, results for component testing shows that our proposed crowd model has successfully produced crowd trajectories that are similar to the real crowd data with an accuracy of 90%. Meanwhile, for the qualitative validation, the proposed model is able to produce collective types of self-organized crowd behaviours such as lane formation, counter flow formation and corner hugging formation. Furthermore, the model has also been validated using the fundamental diagram

Crowd Modelling Validation for Modified Social Force Model

Crowd modeling is mainly used to observe and analyze the movement pattern of the crowd, including their behavior with the influence of building geometry. It also has been used widely in many application areas such as for transportation services, urban planning and event planning. Representation of crowd dynamics using a simulation tool is useful in various crowd studies, where experiments with humans are too dangerous and not practical to be implemented. As to ensure the validity and accuracy of the developed simulation model, it has to be validated with the real data, in which most of recent crowd modeling works are lacking. Therefore, in this paper, we propose three types of approaches to validate our proposed crowd simulation model, the Magnetic Social Force Model, which are the component testing, qualitative validation and quantitative validation. Real data of crowd movement at concourse area of a train station in Kuala Lumpur has been used for the validation purpose in this work. By comparing the simulation analysis with the real data, results for component testing shows that our proposed crowd model has successfully produced crowd trajectories that are similar to the real crowd data with an accuracy of 90%. Meanwhile, for the qualitative validation, the proposed model is able to produce collective types of self-organized crowd behaviours such as lane formation, counter flow formation and corner hugging formation. Furthermore, the model has also been validated using the fundamental diagram