978-1-4577-1884-7/11/$26.00 ©2011 IEEE

Agent-Based Personal Monitoring System Simulation using Type-2 Fuzzy

Azwin Hazrina Ahmad Computer and Information Science Department

Universiti Teknologi PETRONAS Tronoh, Perak, MALAYSIA

azwinahmad@gmail.com

Jafreezal Jaafar Computer and Information Science Department

Universiti Teknologi PETRONAS Tronoh, Perak, MALAYSIA jafreez@petronas.com.my

Ahmad Kamil Mahmood Computer and Information Science Department

Universiti Teknologi PETRONAS Tronoh, Perak, MALAYSIA kamilmh@petronas.com.my

Abstract�—Towards being a developed country, Malaysia has started the new era where every industry adopting new technologies and innovations to improve their operations, services and profit margin. In the middle of this modernization, there is a part where each industry must obey to. It�’s the safety issue. In Malaysia, safety awareness still at low level and people seems doesn�’t care about safety which sometime may cause harm to themselves. Worse case if the incidents happen during work. In this paper, we propose an agent-based personal monitoring system using type-2 fuzzy to simulate the monitoring process for our worker at time of hazard operation. This simulation applies for dedicated person who works in hazard place such as oil and gas plant, sewerage main hole, fire rescue and underground electric generation plant in Malaysia. To design and develop this simulation system, we applied the agent-based approach as it ease the designing process by picturing the real entities of stakeholder to agents in the simulation system. Agents in this simulation help in decision making for personal monitoring using type-2 fuzzy. Type-2 fuzzy is the engine of monitoring, where it defines the relationship of input (sensors) and output (actuators) of this particular domain. Type-2 fuzzy basically make precise the uncertain inputs, so that the output will be TRUE and reliable. It�’s crucial to generate this type of output as we are dealing with human �– expert workers life. The combination of agents and type-2 fuzzy is suitable for dynamic environment like this domain.

Keywords-intelligent agent; personal monitoring; type-2 fuzzy;

I. INTRODUCTION The role of Occupational Safety and Health has been in

existence since 120 years ago, in the late 19th century. It has started with Steam Boiler Safety Era and then followed by Machinery Safety Era. After that, it was continued with Industrial Safety Era, Industrial Safety and Hygiene Era and finally Occupational Safety and Health Era that cover every work sector except who work in ship and military [9]. In Malaysia, occupational safety regulation started in Perak

(1878), when Mr. William Givan was appointed as Machinery Inspector in a tin mine. He inspects the steam boiler condition before and after used. That time was called Steam Boiler Safety Era.

After that, Machinery Safety Era (1914-1952) and manage by Mineral Department of Malaysia. From 1953, Industrial Safety Era has started until year 1967. During this era, there are inspectors assigned to inspect not only the steam boiler or machinery safety but also covered all aspect of factory workers safety. But this Machinery Ordinance 1953 is lack of workers safety and health aspect. In 1970, Malaysia continues with Industrial Safety and Hygiene Era. Suggestion to establish a National Institute of Occupational Safety and Health had been carried forward by the department to the National Advisory Council of Occupational Safety and Health in the year 1985[9]. Finally, the Occupational Safety and Health Era were gazettes on February 1994 through Occupational Safety and Health Act 1994 (Act 514) by the Malaysia parliament. The chronology of Safety Industry in Malaysia can be viewed in Figure 1.

Figure 1. Chronology of Occupational Safety in Malaysia

Steam Boiler Safety Era (Before 1914)

Machinery Safety Era (1914 - 1952)

Industrial Safety Era (1953 - 1967)

Industrial Safety and Hygiene Era (1970 - 1994)

Occupational Safety and Health Era (After 1994)

Occupational Safety and Health Act 1994 covers all of the listed industry in Malaysia, such in Table 1, below.

Industries NPD PD D

TOTAL 578 60 55

In any accident during work, usually the owner of the business or organization overlooks the personal monitoring aspect for their workers. Although they hired a Supervisor or Manager, monitoring activities does not achieve hundred percent monitoring at every minutes. Few managers only give speech or reminder to their workers to be extra careful during works. While, workers think they are fully protected by wearing Personal Protective Equipments (PPE), such as helmet, gloves, safety boot and other. There is another part which should be adopted nowadays, to ensure personal safety during works. We don�’t want to lose any trained workers, especially when they are working, as they are helping develop the country. Trained workers are valuable assets for our country that we should pay more attention in reducing the number of injuries and fatalities among them. Based on this issues, we�’re proposing a personal monitoring system which developed using type-2 fuzzy agent to improve the monitoring

process and decrease the number of injuries and fatalities among our professionals.

A. Personal Monitoring Monitoring usually comes with diagnosis. Monitoring

means checking or regulating the performance of a machine, a process or a system [3]. Then, diagnosis is needed as a step to decide the nature and the cause of an irregular condition of a machine, a process, or a system by examining the symptoms. There are many monitoring system has been proposed, such as plant monitoring, chemical monitoring and also personal monitoring, but in this paper, we will discuss about personal monitoring only, which will be embedded with type-2 fuzzy agent system.

Each monitoring system must have its requirements. What do we mean with requirements? For example, personal monitoring for rescue must have requirements such as:

• Support of knowledge sharing • Distributed architecture for collaborative work • Interoperability with other information systems • Dynamic problem solving • Ability to work with uncertain information • Constraint network notation for real-world problem

description and other [5]. It is the same requirements in personal monitoring during

hazard work discussed in this paper. In order to design and develop monitoring system, we need the support of knowledge shared by expert in the particular area. We also need to know the distribution of collaborative work, so that the agent system will follow the correct order of processes. We also need to know if there is any other information system that should attach to our personal monitoring system. In term of designing a successful monitoring system, a dynamic problem solving method is needed, such as intelligent agent-based method and to make the system able to work with uncertain information gather in real world, we use type-2 fuzzy as solution. Both methods will help the diagnosis phase to determine the system condition.

B. Intelligent Agent In this paper we suggest a new approach of personal

monitoring system using agent. The concept of �“agent�” stems from the lexicon of distributed artificial intelligence (DAI) popular in 1970s [1]. Agents represent the real relationship between system and the stakeholders. In our proposed agent-based personal monitoring system, we will use more than one agent. In the agent studies, the used of multiple agents in a system called Multi-Agent System (MAS). An agent is a program that operates autonomously and accomplished unique tasks without direct human supervision while a Multi-Agent System (MAS) is a computational system where several autonomous or semi-autonomous agents interact and cooperate or complete to perform some set of tasks or satisfy some set of goal [2].

Intelligent agents are long-lived software programs which act autonomously, monitor and react to the environment, and

communicate and collaborate with other agents and users [6]. This characteristic of intelligent agent fulfills the personal monitoring system requirements. Working autonomously means that intelligent agent is able to learn from its experience with basic reasoning capabilities. It is also need to be reactive in order to react with outside environment and do negotiation or collaboration with other agents if necessary.

To enhance the ability of personal monitoring system to deal with uncertain information in the system environment, we suggest to embed type-2 fuzzy logic to ensure the stability and reliability of the system. Where the high uncertainty input gather from the sensors will produce an almost precise or crisp output value. This characteristic is very crucial for personal monitoring system as the decision diagnosed by the system will determine the signal transferred to the personal who�’s been monitored. Off course the decision made by the system is based on the expert knowledge, but the calculated output signals processed by type-2 fuzzy agent will affect the professional lives, either they will be harm by the hazard environment or safe from any forecasted danger. The analysis diagram below (Figure 2) designed the personal monitoring system at the very basic level. This design was produced by Prometheus Design Tool (PDT). Which will be discussed in Methodology Section.

Figure 2. Analysis Overview Diagram for Agent-Based Personal Monitoring

System Simulation using Type-2 Fuzzy

C. Type-2 Fuzzy Fuzzy set and fuzzy logic theory are theories which define

natural phenomenon of the imprecision and uncertainty in most of the system in real world. The characteristic of fuzzy set, namely the membership function, is a function whose range is an ordered membership set within the closed interval [3]. Interval in this domain means the range of [0,1]. Hence, the membership values represent the degree to which an object belongs to a fuzzy set. Type-1 fuzzy set has crisp membership function, but different for Type-2 fuzzy. Type-1 fuzzy set is defined in (1) and (2).

A(x) : X [0,1] (1)

A = {(x, A(x) ) | x X } (2)

Type-2 fuzzy is inherited from type-1 fuzzy, while type-1 fuzzy is the basic principle of fuzzy logic. The concept of a type-2 fuzzy set was introduced by Zadeh in 1973 as an

extension of the concept of an ordinary fuzzy set (henceforth called a �“type-1 fuzzy set�”) [4]. The difference between fuzzy logic, type-1 fuzzy and type-2 fuzzy is depends on the fuzzy membership function. Proffessor Lofti A. Zadeh (1965) stated that �“membership�” in a fuzzy set is not a matter of affirmation or denial, but rather a matter of degree [3]. A type-2 fuzzy set is characterized by a fuzzy membership function, i.e., the membership grade for each element of this set is a fuzzy set in [0,1], unlike a type-1 set where the membership grade is a crisp number in [0,1] [4]. Depending on the types of the membership functions of type-2 fuzzy sets, two different types of type-2 fuzzy sets are:

(i) Type-2 fuzzy sets of full type-2 fuzzy set, introduced by Karnik et al., 1999,

(ii)Interval valued type-2 fuzzy sets introduced by Liang and Mendel, 2000 [3].

Type-2 fuzzy set can be defined such as (3) and (4), where à is a fuzzy set and à is a membership function. 0 Ã(x, u)

1, J, is the primary membership function in [0,1] interval and u are the primary membership values [3].

Ã(x) : X [0,1] (3)

à = {((x, u), Ã(x, u)) | (4)

Figure 3. Type-2 Fuzzy Logic System

Figure 3 shows how a Type-2 Fuzzy Logic System working. There are four main components in the Type-2 Fuzzy Logic System, they are:

1. Rules: are either provided by subject experts or are extracted from numerical data, are expressed as a collection of IF-THEN statements [12].

2. Fuzzifier: measured (crisp) inputs are first transformed into fuzzy sets in the Fuzzifier block because it is fuzzy sets and not numbers that activate the rules which are described in terms of fuzzy sets and not numbers [12].

3. Inference: after measurements are fuzzified, the resulting input fuzzy sets are mapped into fuzzy output sets by Inference block. Inference is done one rule at one time. So, at the output of the Inference block, there will be one or more fired-rule fuzzy output sets [12].

4. Output Processing Block: convert the fired-rule output fuzzy sets into a number.

II. PROMETHEUS METHODOLOGY There are several methodologies has been developed for

agent-based system design. Such as Gaia, Tropos and MAS-CommonKADS, but for our Agent-based personal monitoring system design, we used Prometheus methodology. It is purposely developed by its founder, Prof. Lin Padgham from RMIT University, Autralia to design intelligent agent system. Furthermore, Prometheus provides a design tool called Prometheus Design Tool or PDT which will ease the design and development phase for this simulation system. There are strengths of Prometheus methodology, such as:

• It is an interactive methodology rather than a linear one like Waterfall methodology.

• It provides �“Start-to-End�” design process. By using PDT, it helps generate basic programming lines in Java for the agent system.

• It has hierarchical designing process which allows design to be performed at multiple levels of abstraction.

• It provides automatic crosschecking for design artifact between levels of design.

There�’s only three phases involve, which we think it is a

small number of process and cover all aspect of system design. The first phase is Specification Stage, second is Architectural Design Stage and finally Detailed Design Stage. These three phases has it own purpose. For System Specification process, consists of:

• Identification of actors and their interactions with the system, in the form of percepts and actions.

• Developing scenarios illustrating the system�’s operation.

• Identification of the system goals and sub-goals. • Identifying any external data. • Grouping goals and other items into the basic roles of

the system. [7]

The Architectural Design phase uses artifacts produced in the System Specification Phase to determine what agent types will be included in the system and the interaction between these agent [7]. This includes:

• Determine the agent types • Develop the interaction protocols • Develop the system overview diagram

Finally, the Detailed Design Phase, which focuses on

developing the internal structure of each of the agents and how it will achieve its tasks within the system [8]. Each agent is refined in terms of its capabilities, internal events, plans and data structures [7, 8]. The summary of phases in Prometheus methodology can be view in Figure 4.

Figure 4. Prometheus Methodology.

III. PERSONAL MONITORING USING TYPE-2 FUZZY AGENT APPROACH

In personal monitoring system for hazard work, there are lots of input involve such as Professional�’s Breathing Rate, SCBA Breathing Air Tank Level, Professional�’s Motion, Environment Pressure, Surrounding Gas and etc. but in this paper, we only focus for two input gather from sensors (Sensor Agent), they are:

• Breathing Air Tank�’s Level (x1) • Professional�’s Breathing Rate (x2)

Both physical variables above is very important to determine our monitored professional status at every time during their operation. So, time (x3) will be another input variable in this case. So, let y be the diagnostic rules. Sensors, in other word, Sensor Agent, can identify three linguistic values (low, normal, high) for the two physical variables. Li will represent Low Range set. Ni will represent Normal range set and Hi will represent High range set for input data, xi, where i = 1, 2 or 3. Hence, cases will be symbolized by C1, C2, C3, �…, cases are based on the knowledge shared by the domain experts in real world (Chief Agent in the system). Fuzzy Rules can be write as below.

R(0): IF xi is N1 AND x2 is N2 AND x3 is N3

THEN y is C1

�…

R(n): IF xn is Nn AND xn+1 is Nn+1 AND xn+2 is Nn+2

THEN y is Cn

IV. SUMMARY In this paper an agent-based type-2 fuzzy for personal

monitoring has been proposed. The uncertainty of personal monitoring defined using type-2 fuzzy. Type-2 fuzzy is where we defined the expert knowledge of personal monitoring. Henceforth, the diagnostic for the output has been done by the intelligence agents created. Intelligent agents have been proposed in this study due to its characteristic advantage to recognize and filter the input and output to achieve the optimum monitoring system operation. Simulation for type-2 fuzzy agent approach using the real data has been done to test and collect results for comparison and further discussion.

A. Contributions of the Study From this study, we have seen that there are several

contributions from the simulation done. They are summarized below.

• Uncertainty Handling - where the decision calculate using the uncertain or imprecise input gathered from sensors. This is the function of type-2 fuzzy logic embedded in the system.

• Type-2 Fuzzy Embedded in Intelligent Agent �– the characteristic of intelligent agent, such as, autonomous and reactive make is the monitoring and diagnosis process in the system, where unnecessary signals (input or output) can be remove from system.

• Reduce Human Expert Dependencies �– with the

existence of robust and reliable personal monitoring system, this will optimize the monitoring rate up to 99.9% at every minutes and monitoring through monitor panel will reduce dependencies of human chief operation.

B. Further Work This paper only focuses on the agent-based system using

type-2 fuzzy for personal monitoring during hazard work. The system expected to be reliable in real time operation. For further work, we hope that the idea of fuzzy-based agent can be applied to other field of industry under Occupational Safety and Health, such as, Chemical, Oil and Gas, Marine, Agriculture, Sewerage, Electric Generation and other industry. We�’re confident that this work can be realized as fuzzy has been used for washer machine system, air-condition system and lot more since several decades ago. So, there are possibilities that fuzzy-based agent can be used for high-level and complex system as it has intelligent characteristic. We also encourage any parties to do further research for hardware suitable for this system brain.

ACKNOWLEDGMENT We would like to thanks Universiti Teknologi PETRONAS

for the supports given including STIRF grant awarded to this research project started from Jan 2011.

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