Decision Support System based Site Quality Evaluation for Plantation

Site quality evaluation is an important foundation for decision-making and planning in forest management and provides scientific decision support and guidance for the sustainable development of forests and commercial plantations. Site index and site form models were constructed and subsequently compared utilizing fir (Cunninghamia lanceolata) plantations in Nanping City, Fujian Province, China. This papers aim was to construct a site quality classification model, conduct further analysis on the effects of different site factors on the quality of the site, and achieve an assessment of site quality for Chinese fir plantations. An algebraic difference approach was used to establish a site index model and a site form model for Chinese fir in Fujian Province. The suitability of the two models was compared using model accuracy analysis and partial correlation, and the optimal model was chosen for classifying the site quality of the stands. On this basis, a site quality classification model was established using the random forest algorithm, and the importance of each site factor was determined through importance ranking in terms of their impact on site quality. Within the study area, the R2 of the site index model results was 0.581, and the R2 values of the five site form models based on different reference breast diameters, ranked from high to low, were 0.894, 0.886, 0.884, 0.880, and 0.865. The bias correlation coefficient between site form and stand volume was 0.71, and the bias correlation coefficient between site index and stand volume was 0.52. The results confirmed that the site form model is better suited for evaluating the site quality of Chinese fir plantations. The random forest-based site form classification model had a high classification accuracy with a generalization accuracy of 0.87. The factors that had the greatest impact on site form were altitude, canopy closure, and slope gradient, whereas landform had the smallest impact on site form. These results can provide a reference for the evaluation of the site quality of plantations and natural forests in southern China to ensure the long-term sustainable use of forest resources.

On the design of group decision support systems

-In industrial plants, the profitability of the plant is significantly affected by the quality of machines maintenance. To ensure continuous production, the high valued machines should be kept in good working conditions. This brings plants to search for means to control and reduce equipment failures. When faults emerge in plants, appropriate actions for fault diagnosis and troubleshooting must be executed promptly and effectively to prevent large costs due to breakdowns. To provide reliable and effective maintenance support, the aid of advanced decision support technology utilizing previous repair experience is of crucial importance for the expert operators as it provides them valuable troubleshooting clues for new faults. Artificial intelligence (AI) technology, particularly, knowledge-based approach is promising for this domain. It captures efficiency of problem solving expertise from the domain experts; guides the expert operators in rapid fault detection and troubleshooting. This paper focuses on the design and development of a Knowledge-Intensive Decision Support System (KI-DSS) for Maintenance, Repair and Service in industrial plants to support better maintenance decision and improve maintenance efficiency. With integration of case-based Reasoning and ontology, the Ki- DSS not only carries out data matching retrieval, but also performs semantic associated data access which is important for intelligent knowledge retrieval in decision support system. A case is executed to illustrate the use of the proposed KI-DSS to show the feasibility of our approach and the benefit of the ontology support.

The current paper deals with the design and development of a web-based group decision support system for the evaluation and selection of research projects. The paper involves the analysis of the system’s requirements, the definition of its functional structure, the development of an algorithm for the evaluation and ranking of research projects, the development of a related database and of a user interface along with the writing of the business logic of the system.

A web-based GIS Decision Support System for managing and planning USDA's Conservation Reserve Program (CRP)

This paper describes about the design and development of UMN scholarship decision support system using Profile Matching method. At Universitas Multimedia Nusantara (UMN), there are scholarships for students in order to ease the burden of education costs. There are achievement scholarships, scholarships for students whose parents died, and non-academic scholarships. Decision support systems play an important role in the termination of the final decision, and therefore this application is built by implementing the method of Profile Matching to aid in the selection of the scholarship decisions for students whose parent were died and non-academic scholarship. Profile Matching is a method that aims at taking decisions by assuming that there is an ideal level of predictor variables that must be met by the subjects studied, instead of the minimum rate that must be met or passed. From the results, it can be concluded that this method successfully implemented into applications that can help in decision making process. Result of user satisfaction level for this application is 72%, the result of manual calculation with the application calculation results is not much different so that this application can assist in supporting the UMN scholarship decision. Index Terms” decision support system, non-academic scholarship, Profile Matching, scholarships for student whose parents died, UMN

SUMMARY This paper reports on POEMS (Post-Operative Expert Medical System), which incorporates a diagnostic decision support system and a learning apprentice. The former embodies an object—oriented knowledge base and an incremental diagnostic engine which is based on a temporal modeller and a constraint based diagnoser. It employs an active user—interaction methodology for acquiring patient data, and a collaborative question- answering facility. The decision support system provides assistance to the doctors in order to reach diagnostic decisions, and helps to discriminate between multiple diagnoses. The learning apprentice actively acquires new diagnostic knowledge from patient cases and the medical expert's criticism of its diagnostic actions. It is based on an integrated machine learning methodology, Extended Exemplar—Based Learning (EExBL), which draws on exemplar—based learning, explanation—based learning, and similarity—based learning.

It is generally accepted that adaptive design methods and flexible constructs facilitate the design and implementation of decision support systems. Flexible constructs in the form of decision support system generators have been proposed for the timely construction of specific decision support systems. Effective communication, however, underlies the success of applying adaptive design methods and creating and modifying decision support system generators. Accordingly, we propose to investigate communication as a mode of action that allows the participants to structure and to exert control over the design environment. A descriptive model is presented that describes how different modes of communicative action are used to enhance the prospects of success when building decision support systems.

Stem-analysis data from dominant and codominant trees were collected from 383 plots located in fully stocked, even-aged, undisturbed mature jack pine stands. Separate site index curves were independently formulated for four regions of northern Ontario using the Newnham constrained nonlinear regression model; these formulations were used for comparing regional site index curves at three levels of site index (10 m, 15 m and 20 m).Comparisons showed that no significant differences existed between the four regional curves as well as with previously published site index curves for the North Central Region. Each of the four regions had similar polymorphic height-growth patterns; therefore, data for the four regions were combined and a single formulation was used to develop a polymorphic set of site index curves for all of northern Ontario. We found that poor sites in each region had almost linear height growth up to 100 years breast-height age, but for each region height growth became more curvilinear with increasing site index. The recommended site index curves for northern Ontario are based on a formulation using only data from plots 100 years and less but this formulation was not significantly different from a formulation using only data from plots 80 years and less, or a formulation that included all data from plots older than 100 years breast-height age.Comparisons were made between our northern Ontario curves and other jack pine site index curves for Ontario as well as curves for other areas of Canada and the United States. These comparisons generally showed considerable older age differences. Reasons for these differences are uncertain but could be due to differences in the amount and kind of data used for these other curves, could be due to differences in analytical methods, or could be due to regional differences in climate, soil and topography. Key words: site quality evaluation, polymorphic height growth, regional site index curves, site index prediction equations, comparisons among site index curves.

Knowledge acquisition for the design of clinical decision support systems can be facilitated when clinical practice guidelines serve as a knowledge source. We describe application of the Guideline Elements Model (GEM) in the design of a decision support system to promote smoking cessation. Following selection of relevant recommendations and markup of knowledge components with the GEM Cutter editor, the Extractor stylesheet was used to create a list of decision variables and actions for further processing. Decision variables and actions that reflect similar concepts were consolidated. Action types were identified. Extracting the critical concepts from the narrative text facilitates clarification of necessary content. The guideline-centric approach promotes accurate translation of guideline knowledge.

Background The authors study the development of decision support system (DSS) for designing the bottom-hole assembly (BHA) while drilling, as well as the development of software that implements the DSS. DSS includes expert system, knowledge base, analytics and modeling subsystem, database of drilling equipment. The analytics and modeling subsystems contain algorithms for drilling calculation of the optimal BHA parameters in accordance with the requirements for the well profile, rate of penetration and reduction of sticking danger. The authors discuss how to use the developed expert system as part of an automated control system for directional well drilling. The studied drilling equipment consists of: drill bits, calibrators, downhole screw motors, drilling jar, heavy-weight drill pipes and heavy-wall drill pipes. Analyzed the main parameters of drilling equipment affect sticking danger, rate of penetration and well trajectory. The developed database structure of drilling equipment allows you to calculate the above parameters. The result of the work of the decision-making software is a report defining the types and sequence of installation of the necessary downhole drilling equipment. Aims and Objectives For DSS development for the BHA designing while drilling is necessary to develop an expert system, a knowledge base, analysis and modeling subsystem, a database of drilling equipment to implement this DSS as a part of an automated control system for directional well drilling. Results The authors developed an ontological model for describing and choosing effective BHA design by the Protege software based on evaluation and analysis of the main attributes of BHA selection experience. The authors also developed the drilling equipment database structure for BHA equipment selection based on deductive inference mechanism for decision rules of the knowledge base.

One of the main challenges for architects and technicians is the efficient management of the built environment, in response to the growing deterioration and obsolescence in the building stock. This research introduces the design, development and application of a novel decision support system that assesses the multidisciplinary advantages or disadvantages of different intervention strategies, mainly focused on schools and social housing. The concept of the model aims to gather, interrelate and weight different renovation factors and variables, according to technical, social, energy and economic parameters, quantifying results on the impacts, consequences and benefits of each renovation strategy and providing practical outcomes in the design, construction, management and maintenance stages. This study uses schools and multi-family buildings, located in southern Europe, to apply and test the system iteratively in both building typologies, serving to adjust the calculation model and demonstrate its operation and replicability. The results are classified according to different intensity levels with their corresponding design alternatives along with a graphical output of results for decision-making. This model is expected to contribute to policy-making by introducing new theoretical and practical renovation guidelines, with a rational adjustment of proposals and ensuring effective and feasible action strategies in the built environment.

PT. Jayatama Selaras is a manufacturer of plastic packaging in Indonesia. The problem faced by the company is the delay in the arrival of raw materials and the quality of raw materials that are not in accordance with the specifications provided by the company, so the process of returning or re-ordering needs to be carried out to new suppliers, which increases the time needed to procure raw materials. In addition, there is no integrated system in data storage that is related to the process of procuring raw materials, such as supplier data, and raw material data. The purpose of this study is to design a decision support system in providing an objective selection of alternative suppliers for the Purchasing Department of PT. Jayatama Selaras. The design of a decision support system starts with analyzing the current system using the PIECES framework. Next is creating a database using MySQL based on data management components. The next system design is designing component management models. The model used in the design of decision support systems at PT. Jayatama Selaras is AHP and TOPSIS. The last stage is designing components of dialogue management or user interfaces using the Visual Basic programming language. The results of the decision support system design show that the system can determine the best alternative suppliers, for PVC and LDPE raw materials, for LDPE raw materials to produce alternative sequences of suppliers PT. Indo Thai Trading, PT. Itochu Indonesia, PT. Tempo Nagadi Trading, PT. Kharisma Karya Plast, PT. Akino Indonesia Trading, PT. Panca Budi Pratama and PT. Unipack Plasindo. In addition, a decision support system designed to produce a database of existing suppliers and facilitate the ordering of raw materials to suppliers. The design of this decision support system is addressed to the purchasing department, production department, quality control department, and accounting department.

Naturalistic decision making (NDM) describes how people make decisions with time pressure and other complexities in familiar and meaningful environments. The complexities of NDM, although essential for a thorough understanding of experts, have limited the use of NDM in decision support system (DSS) design and decision analysis. There have been attempts to computationally model NDM theories; however, to do so has required significant programming skill, resulting in opaque models while still leaving significant portions of the theories unrepresented. To provide a path toward simple, representative, mathematical models of NDM, we present a general mathematical form that can model most of the components of the judgment and decision-making strategies from the fast-and-frugal heuristics (FFH) program. As a case study, the NDM quick test process was transformed into representative FFH components and then into mathematical form. The results show that the quick test process is similar to the FFH strategy tallying, which itself is a type of fast-and-frugal tree. Although the mathematical form does not capture all the cognitive processes at work, it does provide a simple, representative form for use in DSS design and decision analysis. Moreover, the model’s basis in the extensive human-subjects, mathematical, and computational analyses completed by the FFH program provides another method for integrating FFH and NDM by providing (a) a framework for generating constructive questions about how NDM theories account for FFH components; (b) a basis for prescriptive NDM decision support tools that are easy to communicate, understand, and apply; and (c) a method for approximating experience, expertise, and time pressure.

The inference process in many existing artificially intelligent embedded systems is mostly based on a snapshot of incoming input data. It may sometimes be inappropriate to make a decision based on these current input data. The ability to suspend the current inference process and reactivate it upon specified conditions is crucial in many real-time applications. The objective of this research is to design and implement a decision support system that will detect time-related events. This system allows a representation of temporal conditions and actions so that temporal inference invocation, suspension and reactivation are possible.This decision support system consists of three major modules: the Development Engine (DE), the Knowledge Base Manager (KBM), and the Time Sensitive Inference Engine (TSIE).The development engine module allows experts to specify their knowledge in terms of rules, especially with temporal conditions; for example, “If water level is below 10 after the valve B is turned on for 2 minutes, state is warning”, and “If state is warning and the temperature of the reactor is above 1000 degree for 30 seconds, state is alarm and turn on the water pump”The user interface in this development Engine module is completely interactive, and decision tables are the tool used for rule specification. These rules will be automatically checked for inconsistency, redundancy and then stored in the knowledge base for later use by the time sensitive inference engine.A DBMS and a data base which contains historical data are required for analysis purposes. Existing database packages can be used for this.The knowledge base manager module is used to maintain and monitor the created knowledge base. The KBM also plays a role as an interface between the DBMS and the TSIE, which accesses the data base through function calls to the DBMS program. This makes the data base acceptable to TSIE.The time sensitive inference engine performs the inference and control processes on the on-line process data. These processes may be activated, suspended, and reactivated depending on the rules set by the expert. Two major sub-modules of the time sensitive inference engine are the Event Manager and the Action Executive. The event manager is responsible for periodically checking the event list, which keeps both historical and predicted events. The action executive monitors the on-line control of the system.The system is being developed on IBM personal computer. It is intended to support experts in building up knowledge bases and to support realtime process control. If users are involved in the inference process, it can be expanded with a user interface, which of course requires some kind of checking function against incomplete requests or contradictory input data. Applications of such a system would include manufacture automation systems, air traffic monitoring systems, financial market analysis and investment systems, etc.