37 research outputs found
Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation
Monte Carlo tree search (MCTS) is extremely popular in computer Go which
determines each action by enormous simulations in a broad and deep search tree.
However, human experts select most actions by pattern analysis and careful
evaluation rather than brute search of millions of future nteractions. In this
paper, we propose a computer Go system that follows experts way of thinking and
playing. Our system consists of two parts. The first part is a novel deep
alternative neural network (DANN) used to generate candidates of next move.
Compared with existing deep convolutional neural network (DCNN), DANN inserts
recurrent layer after each convolutional layer and stacks them in an
alternative manner. We show such setting can preserve more contexts of local
features and its evolutions which are beneficial for move prediction. The
second part is a long-term evaluation (LTE) module used to provide a reliable
evaluation of candidates rather than a single probability from move predictor.
This is consistent with human experts nature of playing since they can foresee
tens of steps to give an accurate estimation of candidates. In our system, for
each candidate, LTE calculates a cumulative reward after several future
interactions when local variations are settled. Combining criteria from the two
parts, our system determines the optimal choice of next move. For more
comprehensive experiments, we introduce a new professional Go dataset (PGD),
consisting of 253233 professional records. Experiments on GoGoD and PGD
datasets show the DANN can substantially improve performance of move prediction
over pure DCNN. When combining LTE, our system outperforms most relevant
approaches and open engines based on MCTS.Comment: AAAI 201
A Two-Stage GIS-Based Suitability Model for Siting Biomass-to-Biofuel Plants and its Application in West Virginia, USA
Woody biomass has been considered of low value because the cost of removal generally exceeded market price. New, valued-added markets to offset removal costs are necessary for utilization to be effective. In recent years the use of biomass as feedstock for biofuel production in the United States has been on the rise. A variety of liquid fuels can be produced from woody biomass; ethanol is one of the most promising. This study presents a two-stage approach to selecting woody biomass-based biofuel plants using Geographical Information System (GIS) spatial analysis and the multi-criteria analysis ranking algorithm of compromise programming. Site suitability was evaluated to minimize direct cost for investors and potential negative environmental impacts. The first step was to create a site suitability index using a linear fuzzy logic prediction model. The model involved 15 variables in three factor groups: (1) general physical conditions, (2) costs, and (3) environmental factors. The weights of the cost factors were determined using pairwise comparisons in the Analytical Hierarchy Process (AHP). The value of site suitability was reclassified into three categories (non-suitable, low-suitable, and high-suitable) using different classification methods. With a feasible plant location defined as an industrial site within the most suitable area, the second stage of the analysis used compromise programming to compare the potential sites. The criteria used to rank the potential sites included fuzzy distance to woody biomass, highways, railways, commercial airports, communities, and available parcel size. The AHP was used to compute the relative importance of each criterion. The top ten suitable sites were determined, and sensitivity analyses were conducted to derive the most preferred sites. The approach was successful in taking a large amount of non-commensurate spatial data and integrating a site-based ranking algorithm to find the top locations for biomass plants. It also has great potential and applicability to other suitability and site selection studies
Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography
Numerous sleep disorders are characterised by movement during sleep, these
include rapid-eye movement sleep behaviour disorder (RBD) and periodic limb
movement disorder. The process of diagnosing movement related sleep disorders
requires laborious and time-consuming visual analysis of sleep recordings. This
process involves sleep clinicians visually inspecting electromyogram (EMG)
signals to identify abnormal movements. The distribution of characteristics
that represent movement can be diverse and varied, ranging from brief moments
of tensing to violent outbursts. This study proposes a framework for automated
limb-movement detection by fusing data from two EMG sensors (from the left and
right limb) through a Dirichlet process mixture model. Several features are
extracted from 10 second mini-epochs, where each mini-epoch has been classified
as 'leg-movement' or 'no leg-movement' based on annotations of movement from
sleep clinicians. The distributions of the features from each category can be
estimated accurately using Gaussian mixture models with the Dirichlet process
as a prior. The available dataset includes 36 participants that have all been
diagnosed with RBD. The performance of this framework was evaluated by a
10-fold cross validation scheme (participant independent). The study was
compared to a random forest model and outperformed it with a mean accuracy,
sensitivity, and specificity of 94\%, 48\%, and 95\%, respectively. These
results demonstrate the ability of this framework to automate the detection of
limb movement for the potential application of assisting clinical diagnosis and
decision-making
An Analysis of Appalachian Hardwood Products in the Chinese Market
To improve information on log and lumber trade between the Appalachian region of the United States and China, 50 Chinese firms identified as potential and current hardwood products buyers were surveyed using a stratified random sampling method from January to May 2008. A series of questions were posed on the importance of certain attributes of the US products and how to make the trading process more efficient. Sampled information included business activities, location(s), annual sales, product distribution of firms importing hardwood products, customer satisfaction, potential barriers to trade, principal sources of supply, product types, grades, and species of hardwood products imported from the United States. Results indicated that the Appalachian region of the United States is an important hardwood source and will continue to play an important role in the Chinese market. Red and white oaks were the most frequently imported species, followed by hard (sugar) maple, black cherry, soft maple, and ash. The Appalachian hardwood logs entered the markets largely in east and north central China, whereas the hardwood lumber importers were mainly distributed in east, south, and north central China. Some ongoing issues such as the impact of Russia's log tariff, the Lacey Act, and others on China's wood supply were also raised. The results should be helpful for Appalachian hardwood producers to further explore opportunities to promote their products in the Chinese markets
Economic Feasibility of a Woody Biomass- Based Ethanol Plant in Central Appalachia
A mixed-integer programming model is developed to assess the economic feasibility of siting
a woody biomass-based ethanol facility in the central Appalachian hardwood region. The
model maximizes the net present value (NPV) of a facility over its economic life. Model inputs
include biomass availability, biomass handling system type, plant investment and capacity,
transportation logistics, feedstock and product pricing, project financing, and taxes. Four
alternative woody biomass handling systems, which include all processes from stand to plant, are
considered. Eleven possible plant locations were identified based on site selection requirements.
Results showed that the optimal plant location was in Buckhannon, West Virginia. The NPV of
the plant with a demand of 2,000 dry tons of woody biomass per day varied from 84.51 million among the systems over a 20-year plant life. Internal rate of return (IRR) of the
facility averaged 18.67% for the base case scenario. Average ethanol production costs were
approximately 2.08 per gallon. Production costs were most impacted by biomass
availability, mill residue purchase price, plant investment and capacity, ethanol yield, and
financing. Findings suggest that a woody biomass-based ethanol facility in central Appalachia
could be economically feasible under certain operational scenarios