The Experts below are selected from a list of 15279 Experts worldwide ranked by ideXlab platform
Jingxin Wang - One of the best experts on this subject based on the ideXlab platform.
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an integrated 3d Log processing optimization system for hardwood sawmills in central appalachia usa
Computers and Electronics in Agriculture, 2012Co-Authors: Jingxin WangAbstract:An integrated 3D Log processing optimization system was developed to perform 3D Log Generation, opening face determination, headrig Log sawing simulation, flitch edging and trimming simulation, cant resawing, and lumber grading. A circular cross-section model together with 3D modeling techniques were used to reconstruct 3D virtual Logs. Internal Log defects (knots) were depicted using a cone model with apex at the central axis of the Log. Heuristic and dynamic programming (DP) algorithms were developed to determine the best opening face, primary Log sawing, edging and trimming, and cant resawing optimization. The National Hardwood Lumber Association (NHLA) grading rules were computerized and incorporated into the system for lumber grading. Sawing methods considered in the system include live sawing, cant sawing, grade sawing, and multiple-thickness sawing. The system was tested using field data collected at two central Appalachian hardwood sawmills. Results showed that using the optimization system can significantly improve lumber value recovery. The optimization system can assist mill managers and operators in efficiently utilizing raw materials and increasing their overall competitiveness in the ever-changing forest products market.
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development of a 3d Log sawing optimization system for small sawmills in central appalachia us
Wood and Fiber Science, 2011Co-Authors: Jingxin Wang, Edward ThomasAbstract:A 3D Log sawing optimization system was developed to perform Log Generation, opening face determination, sawing simulation, and lumber grading using 3D modeling techniques. Heuristic and dynamic programming algorithms were used to determine opening face and grade sawing optimization. Positions and shapes of internal Log defects were predicted using a model developed by the USDA Forest Service. Lumber grading procedures were based on National Hardwood Lumber Association rules. The system was validated through comparisons with sawmill lumber values. External characteristics of Logs, including length, large-end and small-end diameters, diameters at each foot, and defects were collected from five local sawmills in central Appalachia. Results indicated that hardwood sawmills have the potential to increase lumber value through optimal opening face and sawing optimizations. With these optimizations, average lumber value recovery could be increased by 10.01% using the heuristic algorithm or 14.21% using the dynamic programming algorithm. Lumber grade was improved significantly by using the optimal algorithms. For example, recovery of select or higher grade lumber increased 16-30%. This optimization system would help small sawmill operators improve their processing performance and improve industry competitiveness.
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development of a 3d Log processing optimization system for small scale sawmills to maximize profits and yields from central appalachian hardwoods
2011Co-Authors: Jingxin WangAbstract:The current status of Log sawing practices in small hardwood sawmills across West Virginia was investigated and the effects of Log sawing practices on lumber recovery evaluated. A total of 230 Logs two species, red oak (Quercus rubra) and yellow-poplar (Liriodendron tulipifera), were measured in five typical hardwood sawmills in the state. Log characteristics such as length, diameter, sweep, taper, and ellipticality were measured. Additionally, the characteristics of sawing equipment such as headrig type, headrig kerf width, and sawing thickness variation were recorded. A general linear model (GLM) was developed using Statistical Analysis System (SAS) to analyze the relationship between lumber recovery and the characteristics of Logs and sawing equipment for small sawmills in West Virginia. The results showed that the factors of Log grade, Log diameter, species, Log sweep, Log length, different sawmills, the interaction between Log species and grade, and the interaction between Log species and Log length had significant impacts on volume recovery. Log grade, Log species and headrig type had significant effects on value recovery. Hardwood lumber production includes a sequence of interrelated operations. Methods to optimize the entire lumber production process and increase lumber recovery are important issues for forest products manufacturers. Therefore, a 3D Log sawing optimization system was developed to perform 3D Log Generation, opening face determination, headrig Log sawing simulation, cant resawing, and lumber grading. External Log characteristics such as length, largeend and small-end diameters, diameters at each foot, and external defects were collected from five local sawmills in central Appalachia. The positions and shapes of internal Log defects were predicted using a model developed by the USDA Forest Service. 3D modeling techniques were applied to reconstruct a 3D virtual Log that included internal defects. Heuristic and dynamic programming algorithms were developed to determine the opening face and grade sawing optimization. The National Hardwood Lumber Association (NHLA) grading rules were computerized and incorporated into the system to perform lumber grading. Preliminary results have shown that hardwood sawmills have the potential to increase lumber value by determining the optimal opening face and optimizing the sawing patterns. Our study showed that without flitch edging and trimming, the average lumber value recovery in the sawmills could be increased by 10.01 percent using a heuristic algorithm or 14.21 percent using a dynamic programming algorithm, respectively. An optimal 3D visualization system was developed for edging and trimming of rough lumber in central Appalachian. Exhaustive search procedures and a dynamic programming algorithm were employed to achieve the optimal edging and trimming solution, respectively. An optimal procedure was also developed to grade hardwood lumber based on the National Hardwood Lumber Association (NHLA) grading rules. The system was validated through comparisons of the total lumber value generated by the system as compared to values obtained at six local sawmills. A total of 360 boards were measured for specific characteristics including board dimensions, defects, shapes, wane and the results of edging and trimming for each board. Results indicated that lumber value and surface measure from six sawmills could be increased on average by 19.97 percent and 6.2 percent, respectively, by comparing the optimal edging and trimming system with real sawmill operations. A combined optimal edging and trimming algorithm was embedded as a component in the 3D Log sawing optimization system. Multiple sawing methods are allowed in the combined system, including live sawing, cant sawing, grade sawing, and multi-thickness sawing. The system was tested using field data collected at local sawmills in the central Appalachian region. Results showedthat significant gains in lumber value recovery can be achieved by using the 3D Log sawing system as compared to current sawmill practices. By combining primary Log sawing and flitch edging and trimming in a system, better solutions were obtained than when using the model that only considered primary Log sawing. The resulting computer optimization system can assist hardwood sawmill managers and production personnel in efficiently utilizing raw materials and increasing their overall competitiveness in the forest products market.
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a three dimensional optimal sawing system for small sawmills in central appalachia
In: Fei Songlin; Lhotka John M.; Stringer Jeffrey W.; Gottschalk Kurt W.; Miller Gary W. eds. Proceedings 17th central hardwood forest conference; 201, 2011Co-Authors: Jingxin Wang, Edward R ThomasAbstract:A three-dimensional (3D) Log sawing optimization system was developed to perform 3D Log Generation, opening face determination, sawing simulation, and lumber grading. Superfi cial characteristics of Logs such as length, large-end and small-end diameters, and external defects were collected from local sawmills. Internal Log defect positions and shapes were predicted using a USDA Forest Service model. A 3D virtual reconstruction of a Log and its internal defects was generated using 3D modeling techniques. Heuristic and dynamic programming algorithms were developed for opening face determination and sawing optimization while grading procedures were programmed based on the National Hardwood Lumber Association rules. Th e system was validated through comparisons of lumber results generated by the system and by sawmills. Our preliminary results indicated that a signifi cant gain in lumber value can be achieved using this optimization system. Th is study will help small sawmill operators improve their processing performance and understand the impacts of defects on lumber grade, resulting in improved industry competitiveness.
Arthur H M Ter Hofstede - One of the best experts on this subject based on the ideXlab platform.
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quality informed semi automated event Log Generation for process mining
Decision Support Systems, 2020Co-Authors: Robert Andrews, Christopher G J Van Dun, Moe Thandar Wynn, Wolfgang Kratsch, Maximilian Roglinger, Arthur H M Ter HofstedeAbstract:Abstract Process mining, as with any form of data analysis, relies heavily on the quality of input data to generate accurate and reliable results. A fit-for-purpose event Log nearly always requires time-consuming, manual pre-processing to extract events from source data, with data quality dependent on the analyst's domain knowledge and skills. Despite much being written about data quality in general, a generalisable framework for analysing event data quality issues when extracting Logs for process mining remains unrealised. Following the DSR paradigm, we present RDB2Log, a quality-aware, semi-automated approach for extracting event Logs from relational data. We validated RDB2Log's design against design objectives extracted from literature and competing artifacts, evaluated its design and performance with process mining experts, implemented a prototype with a defined set of quality metrics, and applied it in laboratory settings and in a real-world case study. The evaluation shows that RDB2Log is understandable, of relevance in current research, and supports process mining in practice.
Mathias Weske - One of the best experts on this subject based on the ideXlab platform.
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event Log Generation in a health system a case study
Business Process Management, 2020Co-Authors: Simon Remy, Luise Pufahl, Janphilipp Sachs, Erwin P Bottinger, Mathias WeskeAbstract:Process mining has recently gained considerable attention as a family of methods and tools that aim at discovering and analyzing business process executions. Process mining starts with event Logs, i.e., ordered lists of performed activities. Since event data is typically not stored in a process-oriented way, event Logs have to be generated first. Experience shows that event Log Generation takes a substantial effort in process mining projects. This case study reports on the experiences made during the event Log Generation from the real-world data warehouse of a large U.S. health system. As the focal point, the case study looks at activities and processes that are related to the treatment of low back pain. Guided by the main phases of event Log Generation, i.e., extraction, correlation, and abstraction, we report on challenges faced, solutions found, and lessons learned. The paper concludes with future research directions that have been derived from the lessons learned from the case study.
Robert Andrews - One of the best experts on this subject based on the ideXlab platform.
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quality informed semi automated event Log Generation for process mining
Decision Support Systems, 2020Co-Authors: Robert Andrews, Christopher G J Van Dun, Moe Thandar Wynn, Wolfgang Kratsch, Maximilian Roglinger, Arthur H M Ter HofstedeAbstract:Abstract Process mining, as with any form of data analysis, relies heavily on the quality of input data to generate accurate and reliable results. A fit-for-purpose event Log nearly always requires time-consuming, manual pre-processing to extract events from source data, with data quality dependent on the analyst's domain knowledge and skills. Despite much being written about data quality in general, a generalisable framework for analysing event data quality issues when extracting Logs for process mining remains unrealised. Following the DSR paradigm, we present RDB2Log, a quality-aware, semi-automated approach for extracting event Logs from relational data. We validated RDB2Log's design against design objectives extracted from literature and competing artifacts, evaluated its design and performance with process mining experts, implemented a prototype with a defined set of quality metrics, and applied it in laboratory settings and in a real-world case study. The evaluation shows that RDB2Log is understandable, of relevance in current research, and supports process mining in practice.
Vincenzo Valentini - One of the best experts on this subject based on the ideXlab platform.
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ICTAI - A Framework for Event Log Generation and Knowledge Representation for Process Mining in Healthcare
2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 2018Co-Authors: Roberto Gatta, Mauro Vallati, Jacopo Lenkowicz, Calogero Casà, Francesco Cellini, Andrea Damiani, Vincenzo ValentiniAbstract:Process Mining is of growing importance in the healthcare domain, where the quality of delivered services depends on the suitable and efficient execution of processes encoding the vast amount of clinical knowledge gained via the evidence-based medicine paradigm. In particular, to assess and measure the quality of delivered treatments, there is a strong interest in tools able to perform conformance checking. In process mining for the healthcare domain, a number of major challenges are posed by: (i) the complexity of involved data, that refers to patients' aspects such as disease, behaviour, clinical history, psychoLogy, etc; (ii) the availability of data, that come from the heterogeneous, fragmented and scant connected healthcare system; and (iii) the wide range of available standards for communication (DICOM, IHE, etc.) or data representation (ICD9, SNOMED, etc.) purposes. To effectively perform process mining in the healthcare domain, it is crucial to build event Logs capturing all the steps of running processes, which have to be derived by the knowledge stored in the Electronic Health Records. It is therefore crucial to cope with aforementioned data-related challenges. In this paper, we aim at supporting the exploitation of process mining in the healthcare domain, particularly with regards to conformance checking. We therefore introduce a set of specifically-designed techniques, provided as a suite of software packages written in R. In particular, the suite provides a flexible and agile way to automatically and reliably build Event Log from clinical data sources, and to effectively perform conformance checking.
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A Framework for Event Log Generation and Knowledge Representation for Process Mining in Healthcare
2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 2018Co-Authors: Roberto Gatta, Mauro Vallati, Jacopo Lenkowicz, Calogero Casà, Francesco Cellini, Andrea Damiani, Vincenzo ValentiniAbstract:Process Mining is of growing importance in the healthcare domain, where the quality of delivered services depends on the suitable and efficient execution of processes encoding the vast amount of clinical knowledge gained via the evidence-based medicine paradigm. In particular, to assess and measure the quality of delivered treatments, there is a strong interest in tools able to perform conformance checking. In process mining for the healthcare domain, a number of major challenges are posed by: (i) the complexity of involved data, that refers to patients' aspects such as disease, behaviour, clinical history, psychoLogy, etc; (ii) the availability of data, that come from the heterogeneous, fragmented and scant connected healthcare system; and (iii) the wide range of available standards for communication (DICOM, IHE, etc.) or data representation (ICD9, SNOMED, etc.) purposes. To effectively perform process mining in the healthcare domain, it is crucial to build event Logs capturing all the steps of running processes, which have to be derived by the knowledge stored in the Electronic Health Records. It is therefore crucial to cope with aforementioned data-related challenges. In this paper, we aim at supporting the exploitation of process mining in the healthcare domain, particularly with regards to conformance checking. We therefore introduce a set of specifically-designed techniques, provided as a suite of software packages written in R. In particular, the suite provides a flexible and agile way to automatically and reliably build Event Log from clinical data sources, and to effectively perform conformance checking.
