An integrated method for matching forest machinery and a weight-value adjustment

Abstract

Proper matching of forestry machinery is important when raising mechanization levels for forestry production. In the matching process, forestry machinery needs not only expertise, but also improved methods for solving problems. I propose combination of case-based reasoning (CBR) and rule-based reasoning (RBR) by calculating the similarity of quantitative parameters of various forestry machines in an analytical and hierarchical process. I calculated the similarity of machinery used in forest industries to enable better selection and matching of equipment. I propose a weight-value adjusting method based on sums of squares of deviations in which the individual parameter weights were modified in the process of application. During the process of system design, I put forward a design method knowledge base and generated a dynamic web reasoning framework to integrate the processes of forest industry machinery selection and weight-value adjustment. This enables expansion of the scope of the complete system and enhancement of the reasoning efficiency. I demonstrate the validity and practicability of this method using a practical example.

Keywords

forest industry / machinery selection and matching / weight-value determination / reasoning process / integration method

References

[1] Aamodt A, Plaza E. Case-based reasoning, foundation issues, methodological variations, and system approaches. Artificial Intelligence Communications, 1994, 7(1): 39-59.

[2] Bergmann R. Developing industrial case-based reasoning application: The INRECA methodology. 1999, Berlin: Springer-Verlag

[3] Chang KH, Joo SH. Design parameterization and tool integration for CAD-based mechanism optimization. Advances in Engineering Software, 2006, 37(12): 779-796.

[4] Chang PC, Liu CH, Lai RK. A fuzzy case-based reasoning model for sales forecasting in print circuit board industries. Expert Systems with Applications, 2008, 34(3): 2049-2058.

[5] Chou JS. Web-based CBR system applied to early cost budgeting for pavement maintenance project. Expert Systems with Applications, 2009, 36(2): 2947-2960.

[6] Guo Y, Hu J, Peng YH. Research of new strategies for improving CBR system. Artificial Intelligence Review, 2012, 42(1): 1-20.

[7] Huang LH, Qin TF, Ohira T. Studies on preparations and analysis of essential oil from Chinese fir. Journal of Forestry Research, 2004, 15(1): 80-82.

[8] Kowalski Z, Meler-Kapcia M, Zieliński S, Drewka M, Zbigniew K, Maria M, Stefan Z, Marcin D. CBR methodology application in an expert system for aided design ship’s engine room automation. Expert Systems with Applications, 2005, 29(2): 256-263.

[9] Li Q, Liu XH, Liu YH, Yin JL. A CBR-based CAD system for subframe design of aerial work trucks. Lecture Notes in Electrical Engineering, 2012, 141: 389-396.

[10] Liu QS, Xi JT. Case-based parametric design system for test turntable. Expert Systems with Applications, 2011, 38(6): 6508-6516.

[11] MA Y. Discussion about classification methods of forestry and woodworking machines in China. Forestry Machinery & Woodworking Equipment, 2009, 37(1): 4-7.

[12] Massart T, Meuter C, Van Begin L. On the complexity of partial order trace model checking. Information Processing Letters, 2008, 106(3): 120-126.

[13] Peng GL, Chen GF, Wu C, Xin H, Jiang Y. Applying RBR and CBR to develop a VR based integrated system for machining fixture design. Expert Systems with Applications, 2011, 38(1): 26-38.

[14] Shen J, Zhao L, Liu Y. The sampling apparatus of volatile organic compounds for wood composites. Journal of Forestry Research, 2005, 16(2): 153-154.

[15] Tsai CY, Chiu CC. A case-based reasoning system for PCB principal process parameter identification. Expert Systems with Applications, 2007, 32(4): 1183-1193.

[16] Xiang HL, Luo JR, Xiao ZH. Study on the new materials for fiberboard refiner plate of defibrator. Journal of Forestry Research, 2003, 14(1): 89-92.

[17] Yang B, Jeong S, Oh Y, Tan A. Case-based reasoning system with Petri nets for induction motor fault diagnosis. Expert Systems with Applications, 2004, 27(2): 301-311.

[18] Yang ZL, Wang B, Dong XH, Liu H. Expert system of fault diagnosis for gear box in wind turbine. Systems Engineering Procedia, 2012, 4: 189-195.