Mechatronics for Safety, Security and Dependability in a New Era - Arai and Arai Part 11 potx

284 ROI Investments ↔ Costs ↓ Sales ↑ Factory Operations Analysis & Design Act. Input Output Problem Objectives Performance Data Action AS_IS Model & Technology Technology Offer Management Activity (MA) Governance Activity (GA) Model Repository TO_BE Model & Techn- ology Means - …… PI …… …… …… I System ROI Part A Part B pi i PI pi j pi k pi pi pi pi System Waste ↓ Life ↔ Satisfaction ↑ Sales Costs Life Society (Research, Business, Engineering) Ch57-I044963.fm Page 284 Tuesday, August 1, 2006 4:10 PM Ch57-I044963.fm Page 284 Tuesday, August 1, 2006 4:10 PM 284 Repository pa " a chnology Society (Research, Business, Engineering) Figure 1: Linking one decision-objec t hierarchy to the EG AM FACTORY GOVERNANCE Figure 1 (part B) depicts any kind of operations (object system) and its relations to decision activities and the environment. In the figure a high-level Petrinet notation is used, crossed circles (stores) denote persistent data sets, and arcs from places to activities (or processes) liberally follow the control/ re- source/ input/ output conventions of the generic activity model (GAM). The object system performs a function in the environment, and (performance ) objectives are expressed and evaluate d for it. The envi- ronment is the source of inputs and the sink (market) for the outputs. The model is called an Extended Generic Activity Model (EGAM) because it also includes the reflective activities that influence the op- erations. The governance activity expresses objectives for the object system, taking into consideration relevant constraints (natural, social, etc.) that exist for the capital assets in the factory's environment. The management activity monitors the operations and signals a problem if targets are not met. It will call upon the analysis & design activity to analyse the problem of the object system, to create new de- signs (TO_BE model & technology), and to compare performance. Governance and managemen t activ- ities decide about the implementation of a new design in the object system. A Factory is a technical structure (part of the Artifactual Capital) with its operation prescriptions. With- in an environment, and using social flows, this technical structure has allocated Natural Capital (space, time, and material artifacts) to productive uses in such a way that the top-level objectives are achieved. Usually this results in a cellular structure on top of which hierarchies are built for the aggregate reflec- tive activities. Within the Factory, the Social Capital has been refined to meet the various top-level ob- jectives that derive from the Factory's mission statement and from the Factory's embedding in society. Each member of the work force (human capital) has a profile which reflects the various tasks the mem- ber can perform with a performance that is consistent with the related objectives: production tasks, roles in training, safety and health enhancement, disaster reduction, etc. An extended profile also in- cludes the decision-object hierarchies that are related to the operational situations in which the person 285 Ch57-I044963.fm Page 285 Tuesday, August 1, 2006 4:10 PM Ch57-I044963.fm Page 285 Tuesday, August 1, 2006 4:10 PM 285 may find him or herself and to the responsibilities vested in the person. Depending on the kind of event that occurs - expected (prediction), anticipated (contingency), or unexpected (threats to the capital as- sets, adaptiveness) - the person will perform prescribed operations or engage in reflective activities with the purpose to bring the operations or situation in line with objectives. In the most general case, the input can be of any kind, ranging from a routine-production order, over a new guideline on toxic materials, to the occurrence of a disaster or attack. ADVANCED FACTORY GOVERNANCE Figure 1 shows how the sub-hierarchies of objectives, decision variables and performance indicators (for ROI, Part A) are linked to the EGAM for factory operations (Part B). A similar action must be per- formed for all relevant hierarchies of decision objects. As also the factory itself will have a structure, for each organizational element some of the decision objects (its scope, a projection of the overall hier- archy) will matter, and all reflective activities must be assumed. The new demands on factories will re- quire us to do additional objective breakdown for non-financial (i.e., natural, artifactual, social capital assets). As eco-system objectives may be subject to change, the question is how to ensure continuous alignment. For each kind of capital asset, the question is how the reflective activities are best allocated. The more mobile a capital asset is, e.g. financial capital, or the larger share in the time or impact on assets the op- erations have, e.g. manufacturing activities in JIT production facilities, the more need there is for con- trol of the operations themselves. In the case of emergencies on the other hand, there is need for auton- omy and immediate and effective reflection and response. CONCLUSIONS Advanced factory governance systems require a mix of controls and autonomy to continuously achieve objectives for all allocated assets. Basic ideas from Socio-Technical Systems Design - predominantly autonomy and self-regulation - might be combined with characteristics of capital assets, in order to ar- rive at a better balance between the amount of control that is executed by the factory system, and the a- mount of self-control that is left to the teams of human agents. A (cell) situation-specific mix of gov- ernance, management and operational powers with respect to all relevant kinds of assets is expressed in a profile. In relation to natural, human, and social capitals more autonomy is likely. For instance, the a- mount of environmental protection could be left to the discretion of the human stakeholders. But also aspects of safety and security are open to certain human autonomy over the system. Factory governance systems should leave maximum degrees of freedom for the way (order, pace and method) humans exe- cute their work. What is actually left to the discretion of the human beings will influence positively the motivations and subsequent responsible performances of these agents in an intelligent manufacturing system. In a total asset context, where operations are challenged by frequent adjustment of objectives, or by the occurrence of rare unwanted events, Socio-Technical System Design offers instruments to de- termine and maintain a proper balance between self-regulation by human agents and automatic control by the factory-governance system. REFERENCES Bovenkamp M. van de, Jongkind R., Rhijn G. van, Eijnatten F. van, Grote G., Lehtela J., Leskinen T., Little S., Vink P., and Wafler T. (2002). The E/ S tool: IT Support for Ergonomic and Sociotechnical System Design. In: Yamada S. (Ed.), Humacs Project: Organizational Aspects of Human-Machine Co-existing Systems (pp. 67-81). Tokyo, Japan: IMS/HUMACS Consortium, CD-Rom, March. 286 Ch57-I044963.fm Page 286 Tuesday, August 1, 2006 4:10 PM Ch57-I044963.fm Page 286 Tuesday, August 1, 2006 4:10 PM 286 Cochran D.S., Arinez J.F., Duda J.W., and Linck J. (2001). A Decomposition Approach for Manufac- turing System Design. Journal of Manufacturing Systems, 20:6, 371-389. Eijnatten F.M. van (1993). The Paradigm That Changed the Workplace. Assen/ Stockholm: Van Gor- cum/ Arbetslivscentrum, 316 pp. (Anthology: Historical overview of 40 years of STS, with contribu- tions of Hans van Beinum, Fred Emery and Ulbo de Sitter). Eijnatten F.M. van (Ed.) (2002), Intelligent Manufacturing Through Participation: A Participative Simulation Environment for Integral Manufacturing Enterprise Renewal. Hoofddorp, The Nether- lands: TNO Arbeid/ PSIM Consortium/ Tokyo, Japan: IMS/ HUMACS Consortium, CD-Rom, March 2002. Eijnatten F.M. van, and Vink P. (2002). Participative Simulation in the PSIM Project. In: Eijnatten F.M. van (Ed.) (2002). Goossenaerts J.B.M., Reyneri C, and Berg R. van den (2002). The PSIM Environment Architecture. In: Eijnatten F.M. van (Ed.) (2002). Little S., Bovenkamp M. van de, Jongkind R., Waller T., Eijnatten F. van, and Grote G. (2001). The STSD Tool: IT Support for Socio-Technical System Design. In: Johannsen G. (Ed.), Proceedings 8 th IF AC/ IFIP/ IFORS/ IE A Symposium on Analysis, Design, and Evaluation of Human-Machine Sys- tems (pp. 409-414). Kassel: IF AC / HMS. Matsuo T., and Matsuoka Y.(2004). Integrated Virtual Plant Environment for Analyzing Chemical Plant Behavior. In: Taisch M., Filos E., Garello P., Lewis K., and Montorio M. (Eds.), International IMS Forum 2004: Global Challenges in Manufacturing, Part I (pp. 507-514). Milano: Polytecnico di Milano, Department of Economics, Management, and Industrial Engineering, Print: Grafica Sovico srl, Biassono (Milano). Ostrom E. (1990). Governing the Commons: The Evolution of Collective Action. Cambridge, UK: Cambridge University Press. Ostrom E., Gardner R., and Walker J. (1994). Rules, Games, and Common-Pool Resources. Ann Arbor, MI: University of Michigan Press. Rudd M.A. (2004). An Institutional Framework for Designing and Monitoring Ecosystem-Based Fish- eries Management Policy Experiments. Ecological Economics, 48:1, January, 109-124. Shin D.P., Han K., Choi S.J., and Yoon E.S. (2004). Integrated Intelligent Management of Process Safety, Health, Environment and Quality in the IMS/ CHEM Framework. In: Taisch M., Filos E., Garello P., Lewis K., & Montorio M. (Eds.), International IMS Forum 2004: Global Challenges in Manufacturing, Part 1 (pp. 499-506). Milano: Polytecnico di Milano, Department of Economics, Management, and Industrial Engineering, Print: Grafica Sovico srl, Biassono (Milano). Vink P., Eijnatten F.M. van, and Berg R.van den (2002). Participation: The Key to Intelligent Manufac- turing Improvement. In: Yamada S. (Ed.), Humacs Project: Organizational Aspects of Human-Ma- chine Coexisting Systems (pp. 1-9). Tokyo, Japan: IMS/ HUMACS Consortium, CD-Rom, March, Invited paper (key note speech) to the 20 th International Conference on Conceptual Modeling (ER 2001), November 27-30, Yokohama, Japan. International Workshop on Conceptual Modeling of Hu- man/ Organizational/ Social Aspects of Manufacturing Activities, HUMACS 2001. Yamada S. (2002). Global Perspectives of the PSIM Project. In: Eijnatten F.M. van (Ed.), Intelligent Manufacturing Through Participation: A Participative Simulation Environment for Integral Manu- facturing Enterprise Renewal (pp. 1 -8). Hoofddorp, The Netherlands: TNO Arbeid/ PSIM Consorti- um/ Tokyo, Japan: IMS/ HUMACS Consortium, CD-Rom, March 2002. 287 Ch58-I044963.fm Page 287 Tuesday, August 1, 2006 4:39 PM Ch58-I044963.fm Page 287 Tuesday, August 1, 2006 4:39 PM 287 RELATION DIAGRAM BASED PROCESS OPTIMIZATION OF PRODUCTION PREPARATION PROCESS FOR OVERSEA FACTORY Shuichi Sato 1 , Yutaka Inamori 1 , Masaru Nakano 1 , Toshiyuki Suzuki 2 , Nobuaki Miyajima 2 1 Toyota Central R&D Labs., Inc., Nagakute, Aichi, 480-1192, Japan Toyota Motor Corporation, Toyota, Aichi, 471-8571, Japan ABSTRACT This paper proposes the method for the optimization of the production preparation processes for factories in oversea. The method does not use the existing tasks, but the relations between physical designed and measured variables written in a relation diagram. The relation diagram is one of the seven new tools for quality control. The new method can optimize the process based on the physical relations and the essential constraints on the task order with the genetic algorithm. The new technique was evaluated using a hot forging trial process and a 40% improvement of the lead time can be seen in comparison with the sequential trial. KEYWORDS production preparation, process optimization, design structure matrix, relation diagram, genetic algorithm, project scheduling INTRODUCTION For manufacturing companies today, strategic and timely product development is essential to survive. Value chains including the market, the production, and the supply of parts have to be considered for the world-wide point of view. Subsequently, some manufacturing companies are moving their factories abroad. On the other hand, most companies continuously perform quality control activities (QC), in order to produce high quality goods to satisfy consumers. We have developed the process optimization technology that can be applied to the production preparation processes for factories outside of Japan. In order to shorten the lead time of the production preparation for oversea factories, manufacturing companies focus on two points: First, measuring process data such as the temperature of a part after being heated for a hot forging process is focused. Second, the design standard is considered. We analyzed the production preparation process for oversea factories and found the following results. Measuring process data helps dividing big and 288 Ch58-I044963.fm Page 288 Tuesday, August 1, 2006 4:39 PM Ch58-I044963.fm Page 288 Tuesday, August 1, 2006 4:39 PM 288 complicated problems into smaller and simpler sub-problems. Furthermore this reduces the influence of uncontrolled elements in the latter stage of the production preparation. We also found that the design standard can change the dependency relationship between the different tasks. The existing study (Sato et ah, 2003) proposed the optimization technique for the oversea production preparation process by considering the dependency relations between the different tasks, measuring the process data and the design standard together. That approach uses the physical relations between the designed variables, measured process data, and performance measures. By using the physical relations, the dependency relations between tasks are generated in the Design Structure Matrix (Yassine et ah, 1999) and the process is optimized by considering the difference of the verification accuracy among the different trial phases. But that approach has two major problems. One problem is the difficulty to reveal the physical relations in the matrix expression when the number of the design variables, measured process data, and performance measures is large. The other problem is the impossibility to consider the essential constraint of the task order coming from the engineer's experience. The proposed technique has been developed in order to overcome such problems. With the new technique, the engineer writes the physical relation in the relation diagram expression as shown in Figure 1, which is one of the seven new tools of the QC. The new optimization algorithm considering the strong constraints on the task order is proposed based on the Genetic Algorithm (Holland, 1975) with Partial Matched Crossover (Goldberg, 1989). The new technique is evaluated using a hot forging trial process and the result showing at the end of this paper confirms the efficiency of the proposed approach. Cause—•Effect Hardness of work Temperature of work as being heated K \ Underfill | . / '\ Friction of die / \ \ / \ Heating voltage Heating time Quality of material Diameter of row material Figure 1: Relation diagram for physical relations between designed variables APPROACH We focus on the fact that different engineers perform the production preparation in different ways. We think we should consider the relations behind the process, which are more general and don't depend on the individual engineer. We have concluded that the physical cause-effect relations of the object to be designed are the fundamental factors. Figure 2 shows the proposed hierarchical model of the production preparation process. The proposed optimization technique is based on this model. The lowest level is composed of the physical cause-effect relations of the object to be designed and the company's design standards such as the standard design requirement and the standard design sequence. The company's design standards are important factors to compete with rival companies. Tn the middle level, the dependency relations between design and/or preparation tasks exist. The structure shows the dependency relations between the tasks are constrained by the physical cause-effect relations and the company's design standards. The product design and the production preparation process exist on the dependency relations between the tasks and the essential constraints on the task order. The essential 289 Ch58-I044963.fm Page 289 Tuesday, August 1, 2006 4:39 PM Ch58-I044963.fm Page 289 Tuesday, August 1, 2006 4:39 PM 289 constraints on the task order will be described in detail, further below. With respect to such a hierarchal model for the production preparation process, we developed an optimization technique as shown below. Production preparation process Essential constraint of task order Dependency relation of design tasks Company's design standards Physical cause-effect relation of designed object Figure 2: Hierarchical relation model of production preparation process Description in relation diagram The current technique (Sato et ah, 2003) requires the engineer to input the physical relations between the designed variables, measured process data, and performance measures in the matrix. However, adding the relations in the matrix is difficult for most practical cases. We found that the matrix expression is useful to analyze the process, but the engineer is hesitant to use the matrix expression to visualize their knowledge. Incidentally the engineers usually use the seven fundamental tool of the QC as the numerical method for the quality control activity. Furthermore they have the new seven tool of the QC as the linguistic method. These tools are used as the basic techniques for business reengineering and problem solving in the production area. The relation diagram is one of the seven new tools of the QC. This diagram is the method to describe the cause-effect relations if many causes are interacting with each other. Many engineers are familiar with describing the relation diagram for problem solving. The proposed method in this paper uses the relation diagram to visualize the physical relation as seen in Figure 1, and subsequently transforms the diagram to the matrix formation. Optimization algorithm In the actual process, there are many causes that constrain the task order strongly coming from something except for the dependency relation between the tasks. One example is about the time required to complete each task. The engineers have to do the tasks in the earlier stage, which take long time to be performed. Another example is the situation that some tasks have been completed when the target process starts. The optimization algorithm used in the current technique (Sato et al., 2003) cannot consider the essential constraint on the task order except for the dependency relation between tasks to generate the task order. In this paper, one of the modern heuristic methods in the artificial intelligence research field, Genetic Algorithm (GA) (Holland, 1975), is used. This method can consider various constraints flexibly by modifying the fitness function. The expressions of the essential constraints and the chromosome of the GA are explained in the following sections. The crossover operation method and the fitness function to evaluate each chromosome are also described subsequently. 290 Ch58-I044963.fm Page 290 Tuesday, August 1, 2006 4:39 PM Ch58-I044963.fm Page 290 Tuesday, August 1, 2006 4:39 PM 290 Expression of essential constraints In the proposed method, "1 " is assigned for the dependency relations between the tasks as illustrated in Figure 3. "10" is used for the essential constraint coming from something except for the dependency relations between the tasks. For example, if the essential constraint on the task order is that the task 2 has to be performed after the task 5, "10" is assigned to the cell as seen in Figure 3. If there are no dependency relations between the tasks and the essential constraints, the cell contains '0' Essential constraint on task order Tas k Tas k Tas k Tas k Tas k Tas k Tas k Tas k 1 2 3 4 5 6 7 8 Tas k 1 | • 0 0 1 0 0 0 o Tas k 2 | 0 • 0 0 1 0 1 0 Tas k 3 | 0 0 • 0 0 0 0 0 Tas k 4 | 1 ( i • 0 1 0 0 1 10 -tr 0 • 0 0 i , 0 )o 0 0 0 • 0 0 Tas k 7 | 0 0 1 0 1 0 • 0 Tas k 8 | 0 0 0 0 0 o o • Figure 3: Description of essential constraint on task order Expression of chromosome The gene is expressed through the task identification. The chromosome shows the task order through the task identification as seen in Figure 4. Therefore the length of the chromosomes coiTesponds to the number of the target tasks. Identification number of task 4 56 37 22 5 • • • Figure 4: Expression of chromosome Crossover operation If the regular crossover operation (Holland, 1975) is applied to the aforementioned chromosome, the chromosome must have the same genes, i.e., the same task identifications in almost all cases. This means that the chromosome generated by the crossover operation does not express the task order. Therefore, if the regular crossover operation is used, the efficiency of searching the best task order degrades significantly. In the proposed method, the special crossover method called Partially Matched Crossover (Goldberg, 1989) is used. This method was initially developed for the traveling salesman problem (TSP), where the order of the places the salesman should visit is resolved. This approach can also be used to resolve the problem which we handle. Calculation of individual's fitness 291 Ch58-I044963.fm Page 291 Tuesday, August 1, 2006 4:39 PM Ch58-I044963.fm Page 291 Tuesday, August 1, 2006 4:39 PM 291 Each generated chromosome includes the corresponding DSM. The proposed method uses the following functions to calculate the individual's fitness. (1) where m tj shows the element of the line i and the column j in the DSM, N is the number of tasks, C, and C 2 are two coefficients. The first term of Eqn. 1 has the effect to reduce the number of "10" and "1 " in the upper-right field of the matrix. In the DSM expression, the order of the matrix represents the task order, and the "1 " in the upper-right field represents the point for the back loop of the process. Therefore, the first term has the effect to reduce the possibility of the back loop coming from the dependency relations between the tasks and satisfy the essential constraints. The second term shortens the distance from the "10" and/or "1" to the diagonals, which is represented as j-i in the equation as seen in Figure 5. This effectively makes the size of the back loop smaller with the satisfaction of the essential constraint. Distance is equal to 3 Task Task Task Task Task Task Task Task 1 2 3 4 5 6 7 8 Task • 0 0 1 0 0 0 0 CM Task 0 • 0 0 1 0 1 o / a 0 I 0 0 0 0 0 in 1 I • 0 1 0 0 in fa 10 1 i i 0 0 to TasI. ^ 0 jo '0 0 0 \ i I 1. ol of CO 0 0 1 0 1 0 1 0 CO TasI. 0 0 0 0 0 0 0 • Figure 5: Expression of chromosome CASE STUDY The new technique to improve the oversea production preparation was evaluated using a hot forging trial process. This process can be divided into the following three trial phases. • Trial phase with an experimental set up • Domestic trial phase by machines used after starting the production • Overseas trial phase A total of 95 physical parameters in this process are extracted as shown in Table 1. The process optimization using the presented method was able to improve the lead time by around 40%, in comparison with the sequential trial. Furthermore, the proposed method realized the optimized process while satisfying all the essential constraints. Figure 6 shows the part of the matrix which includes the essential constraints on the task order. Figure 6(a) shows the result of the method without consideration for the essential constraints on the task order. The task group in Figure 6 shows the tasks which should be performed together. Task A and Task E compose one group. Figure 6(b) shows the result of the method considering the constraints. The task groups in both cases are the same. But the order of the tasks differs between Figure6(a) and (b). Only the process in Figure 6(b) satisfies the essential constraints. 292 Ch58-I044963.fm Page 292 Tuesday, August 1, 2006 4:39 PM Ch58-I044963.fm Page 292 Tuesday, August 1, 2006 4:39 PM 292 TABLE 1 COMPOSITION OF PHYSICAL PARAMETERS Cateaorv Quality Cost & safety Raw material Cuttinq Heatinq Forqinq Trimminq Thermal refininq Shot blast Total Number of items 9 4 3 6 6 42 16 5 4 95 Task groups Task order Task order TaskB TaskD TaskE Task A TaskF TaskH Taskl TaskG TaskB • 0 0 0 0 ft 0 0 0 TaskD 0 • i i 0 0 0 0 • 1 -9 ft 0 0 0 Tas k A M i • 1 0 o 0| Ip o d -p M s p /° iy o 4 ° l <| | 0 ol 0 l)| 0 • 1 Taskl 0 0 0 0 0 1 1 1 TaskG 0 0 0 U 0 1 1 I TaskB TaskD TaskH Taskl TaskG TaskE Task A Task F TaskC Task B • n 0 0 0 0 0 0 0 Task D 0 • T 1 I 0 0 0 0 TaskH 0 n 1 1 0 0 0 0 Taskl 0 n 1 I 1 10 0 0 0 TaskG 0 n 1 1 • 1 I i 0 Task E 0 n 0 0 10 I 1 -o 0 Task A 0 n 0 0 0 1 • 1 TaskF 0 0 0 0 0 0 1 1 o i = 0 n 0 0 0 0 4 i •1 a) Without use of essential constraints b) With use of essential constraints Figure 6: Efficiency by considering essential constraints CONCLUSION The new technique does not start with the existing tasks, but with the physical cause-effect relations between designed, adjusted, intermediate and goal variables. These physical relations are described in the relation diagram. Required tasks are generated based on these physical cause-effect relations. The proposed technique was evaluated using a hot forging trial process that includes 95 physical variables. An improvement of the lead time of around 40% was realized using a process optimization with the essential constraints, as compared to the sequential trial. REFERENCES Goldberg D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley. Holland J.H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press. Sato S., Inamori Y., Nakano M., Suzuki T. and Miyajima N. (2005). Analysis Method for Overseas Production Preparation Process. Journal of Japan Society of Mechanical Engineering 71:705, 322-329. Yassine A., Falkenburg D. and Chelst K. (1999). Engineering Design Management: An Information Structure Approach. Journal of Production Research 37:13, 2957-297. 293 Ch59-I044963.fm Page 293 Thursday, July 27, 2006 8:59 AM Ch59-I044963.fm Page 293 Thursday, July 27, 2006 8:59 AM 293 CYBER CONCURRENT MANUFACTURING INTEGRATED WITH PROCESS ENGINEERING AND 3D-CG SIMULATION -PRODUCT DESIGN, PRODUCTION SYSTEM DESIGN, AND WORKSTATION SYSTEM DESIGN AS A CASE STUDY ON "CURTAIN WALL" CONSTRUCTION WORK- Kinya Tamaki School of Business Administration, Aoyama Gakuin University, 4^-25 Shibuya, Shibuya-ku, Tokyo, Japan ABSTRACT The research in the last fiscal year (2003), we have indicated a conceptual framework of "Cyber Concurrent Manufacturing (CCM)" system. In order to continue the research in the last fiscal year, a method of modeling in detail by using a Process Engineering tool is proposed. The method is applied to a case study which is to model construction processes of the "curtain wall" installation in a virtual construction site. The feature of this method is to define the total processes with keeping mutual relationship between (1) product design, (2) production system design, and (3) workstation system design. Furthermore, it is able to previously verify the result of the model data of (1) to (3) by various 3D-CG simulators before starting an actual construction. KEYWORDS Cyber manufacturing, process engineering, 3D-CG simulation, construction management, intelligent manufacturing system (IMS), product design, production system design, and workstation system design. INTRODUCTION This research has been performed as a work package study involved in "Innovative, and Parts-oriented Construction (IF7-II)" project. IF7-1 project is one of Intelligent Manufacturing System (IMS) program which was proposed by Japan Ministry of Economic and Trading Industry since 1989. This research member is as follows: Aoyama Gakuin Univ., Waseda Univ., Osaka Univ., Tokyo Institute of Electric and Communication, Shimizu Corp., Tostem Corp., and Hitachi Zosen Information Systems Corp. [...]... of the antenna (a) , and radiation patterns of the antenna in XZplane (b), XY-plane (c), and YZ-plane (d) 308 First, the radiation pattern was measured in the XZ-plane (refer to Figure 1) and the antenna was fully rotated counter-clockwise around the Y-axis Next the radiation patterns were measured in XY- and YZ-planes in a similar manner The measurement results are shown in Figures 2b, 2c, and 2d,... and a microstrip inverted F-antenna (IFA) An inverted-F antenna is a compact antenna with a height of about one tenth of the wavelength (Olmos 2002) The layout of the developed IFA antenna is shown in Figure 1 The IF A antenna consists of two sections: the inverted-L radiating section (a- b-c) and the matching section (b-d-e) The antenna is fed at point a and grounded at point e (Olmos 2002) The smaller... microstrip antenna was attached to a wooden rod, which was placed on a turntable The antenna was placed at a height of 120 cm A sine signal with a frequency of 2.45 GHz and amplitude of 0 dBm was fed to the microstrip antenna from a Rohde & Schwartz SMR20 signal generator The radiation pattern was measured with HP 119 66E double-ridged waveguide horn antenna, which was placed at a height of 120 cm and at a distance... Letters 38:16, 845 - 847 Ali M and Hayes G.J (2000) Analysis of Integrated Inverted-F Antennas for Bluetooth Applications IEEE-APS Conference on Antennas and Propagation for Wireless Communications, Waltham, MA USA, 2 1-2 4 309 DEVELOPMENT OF LOCAL POSITIONING SYSTEM USING BLUETOOTH T Hirota1, S Tanaka1, T Iwasaki1, H Hosaka1, K Sasaki1, M Enomoto1 and H Ando1 1 Graduate School of Frontier Sciences,... for the studied case Other ready-made radio modems are usually designed for sending small, not time-critical packets over long distances Although some of them have adequate bit rates, the latency is usually not presented in data sheets There are also different non-standard transceiver circuits They are available at different bit rates, ranges, modulations and frequency bands Some circuits perform intelligent... controller parameters are shown in Table 1 In the wireless arrangement the lag and lost packets made the system unstable at lower velocity and acceleration gains than in the wired reference setup Because the KV and KA are used to damp the oscillation caused by high proportional gain, also the KP had to be reduced a little As can be seen from Figure 4, the velocity stays almost equally stable at both cases... radiation pattern measurements show that the antenna is quite unidirectional in the XY- and YZ-planes In the XZ-plane the radiation pattern is smaller and its shape is more elliptical than circular This must be taken into account when determining the exact placement of the Bluetooth PCB within the earcup The radiation pattern should be large in horizontal directions, but it may be smaller in vertical... load mitigation and working efficiency paying attention to the workload and the workability of human work Furthermore, the analysis to the posture and work load of human work which attaches vertical material was shown by applying the simulator By performing load analysis of modeling of the process of these human task, or human work, data can be used as basic data these results at the time of creation... readers and communicates with the user mobile MCRS operation may be briefly as follows A reader lists every tag detected in its reading range This list is updated on tag arrivals in or departures from the reading range; all changes are sent to the MGS The MGS automatically informs the user about the movements of those tags that are set to follow-up The user can also check all the tags that are within... especially, is hazardous and can cause permanent hearing loss Traditional passive hearing protectors attenuate noise efficiently and protect the inner ear from loud noise However, when wearing a passive hearing protector, it is almost impossible to use a cellular phone, and important calls may be missed if the ringing tone is not heard It is possible to connect a cellular phone into an electronic hearing . Evaluation of Human-Machine Sys- tems (pp. 40 9-4 14). Kassel: IF AC / HMS. Matsuo T., and Matsuoka Y.(2004). Integrated Virtual Plant Environment for Analyzing Chemical Plant Behavior. In: Taisch. Tamaki School of Business Administration, Aoyama Gakuin University, 4 ^-2 5 Shibuya, Shibuya-ku, Tokyo, Japan ABSTRACT The research in the last fiscal year (2003), we have indicated a conceptual framework. Eijnatten F.M. van, and Berg R.van den (2002). Participation: The Key to Intelligent Manufac- turing Improvement. In: Yamada S. (Ed.), Humacs Project: Organizational Aspects of Human-Ma- chine