Self-organizing Map Neural Network Method Research Articles

Approximate optimal energy management with a high-precision vehicle speed prediction algorithm

A predictive energy management strategy is proposed for plug-in hybrid electric vehicles, which adopts a new high-precision vehicle speed prediction algorithm and a real-time reference state of charge (SOC) trajectory planning method. The speed prediction algorithm includes a modified Markov model based on vehicle speed features recognized by the self-organizing map neural network method, Fourier approximation, and moving average filtering. The prediction accuracy of the algorithm, measured by the root mean square error, is improved by 32.28% and 23.33%, respectively, over that of the standard Markov model when the prediction time is 5 and 10 s. To approach the optimal management strategy for improved vehicle fuel economy, the radial basis function neural network method is utilized to correlate the 10 s predicted vehicle speed with the trajectory slope of the suboptimal SOC for obtaining the reference SOC trajectory, which is applied to the adaptive equivalent consumption minimization strategy based on SOC feedback. The simulation indicates that the fuel consumption by using the proposed energy management strategy is very close to that by the theoretical suboptimal strategy with the shooting method, which leads to a superior fuel economy among the strategies studied.

Analysis of spatiotemporal land cover changes in Inner Mongolia using self-organizing map neural network and grid cells method

Land use has changed dramatically in the Inner Mongolia Autonomous Region because of rapid economic growth and human disturbances. However, little information is available about the medium- and long-term land use changes in this region. The effects of ecological recovery policies have also been evaluated rarely. In this study, we employed the self-organizing map neural network method to identify the land cover changes in Inner Mongolia between 2000 and 2014. MOD13Q1, Landsat, and DMSP/OLS night-time light data were used as the data resources. The dynamic change map was characterized using the grid cell method. The results showed that urban area of Inner Mongolia increased by more than five times during the 15-year study period, while the mining area also increased. In addition, 35.3% of the farmland was changed into grassland, which may have been caused by the “Grain to Green” policy. The most significant environmental issue in Inner Mongolia is the loss of wetland. >40% of the wetland was converted into other land use types between 2000 and 2014. Grassland increased by 6.05%, but areas of open water and woodland remained about the same. In terms of the geographical distribution, cropland increased in the eastern and middle parts of the region. The transformation from wetland to grassland mainly occurred in the north. Grassland degradation occurred in the west. Thus, environmental policy has resulted in some ecological improvements in Inner Mongolia. However, new environmental problems associated with rapid economic development should be addressed in a timely manner.

Techno-Economic analysis of solar photovoltaic power plant for small scale fish processing in Kota Langsa - a case study

In Langsa, fisheries are the sector leaders by fulfilling a capacity of about 6,050 tons per year and on the other hand, fish-aquaculture reaches 1,200 tons per year on average. The fish processing is conducted through catches and aquaculture. The facilities on which this processing takes place are divided into an ice factory unit, a gutting and cutting unit, a drying unit and a curing unit. However, the energy and electricity costs during the production process has become major constraint because of the increase in the fishermen’s production and income. In this study, the potential and cost-effectiveness of photovoltaic solar power plant to meet the energy demands of fish processing units have been analysed. The energy requirements of fish processing units have reached an estimate of 130 kW, while the proposed design of solar photovoltaic electricity generation is of 200 kW in an area of 0,75 hectares. In this analysis, given the closeness between the location of the processing units and the fish supply auctions, the assumption is made that the photovoltaic plants (OTR) were installed on the roof of the building as compared to the solar power plants (OTL) installed on the outside of the location. The results shows that the levelized cost of OTR instalation is IDR 1.115 per kWh, considering 25 years of plant life-span at 10% of discount rate, with a simple payback period of 13.2 years. OTL levelized energy, on the other hand, is at IDR 997.5 per kWh with a simple payback period of 9.6 years. Blood is an essential component of living creatures in the vascular space. For possible disease identification, it can be tested through a blood test, one of which can be seen from the form of red blood cells. The normal and abnormal morphology of the red blood cells of a patient is very helpful to doctors in detecting a disease. With the advancement of digital image processing technology can be used to identify normal and abnormal blood cells of a patient. This research used self-organizing map method to classify the normal and abnormal form of red blood cells in the digital image. The use of self-organizing map neural network method can be implemented to classify the normal and abnormal form of red blood cells in the input image with 93,78% accuracy testing.

The morphological classification of normal and abnormal red blood cell using Self Organizing Map

Blood is an essential component of living creatures in the vascular space. For possible disease identification, it can be tested through a blood test, one of which can be seen from the form of red blood cells. The normal and abnormal morphology of the red blood cells of a patient is very helpful to doctors in detecting a disease. With the advancement of digital image processing technology can be used to identify normal and abnormal blood cells of a patient. This research used self-organizing map method to classify the normal and abnormal form of red blood cells in the digital image. The use of self-organizing map neural network method can be implemented to classify the normal and abnormal form of red blood cells in the input image with 93,78% accuracy testing.

Land Cover Classification and Change Analysis in the Horqin Sandy Land From 1975 to 2007

Observations over the last three decades show that desertification poses a serious threat to the livelihood and productivity of inhabitants of the Horqin Sandy Land region of China. We evaluated the dynamics and trends of changes of land cover in the Horqin Sandy Land by using Landsat archive images from 1975, 1987, 1999, and 2007. We applied two supervised classification methods, the self-organizing map neural network method and the subspace method. Our analyses revealed significant changes to land cover over the period 1975-2007. The area of cropland doubled over the last three decades. This expansion was accompanied by large increases in water consumption and considerable loss of areas of grassland and woodland. Many lakes and rivers shrank rapidly or disappeared in this region between 1975 and 2007. The sandy area expanded rapidly from 1975 to 1987 but gradually slowed thereafter.