Improving LDPC Decoding Performance for 3D TLC NAND Flash by LLR Optimization Scheme for Hard and Soft Decision

Low-density parity-check (LDPC) codes have been widely adopted in NAND flash in recent years to enhance data reliability. There are two types of decoding, hard-decision and soft-decision decoding. However, for the two types, their error correction capability degrades due to inaccurate log-likelihood ratio (LLR) . To improve the LLR accuracy of LDPC decoding, this article proposes LLR optimization schemes, which can be utilized for both hard-decision and soft-decision decoding. First, we build a threshold voltage distribution model for 3D floating gate (FG) triple level cell (TLC) NAND flash. Then, by exploiting the model, we introduce a scheme to quantize LLR during hard-decision and soft-decision decoding. And by amplifying a portion of small LLRs, which is essential in the layer min-sum decoder, more precise LLR can be obtained. For hard-decision decoding, the proposed new modes can significantly improve the decoder’s error correction capability compared with traditional solutions. Soft-decision decoding starts when hard-decision decoding fails. For this part, we study the influence of the reference voltage arrangement of LLR calculation and apply the quantization scheme. The simulation shows that the proposed approach can reduce frame error rate (FER) for several orders of magnitude.

Development and Application of Multi Commodity Multi Location Integrated Model for Manufacturing Industry

This paper presented the development and application of Linear Programming to the modeling of Multi-Commodity Multi-Location production-distribution model for manufacturing industry. The Manufacturing industry has two plants, three depots and twenty retailer’s axis in Lagos. The products are based on how they are packaged; Product 1(P1), Product 2(P2), Product 3(P3) and Product 4(P4). TORA software is used in analyzing the data obtained from the company. Comparing the optimal Multi-Commodity Multi-Location transportation cost of One trillion, Five Hundred And Thirty Billion And Four Hundred And Ninety Million Naira to existing transportation cost of truckload Three Trillion, Five Hundred And Forty Four Billion Naira, the difference is Two Trillion, Thirteen Billion And Five Hundred And Ten Million Naira which is Four Hundred And Two Billion And Seven Hundred And Two Million Naira annually resulting to 56.82 percent gain in profit.

Modeling of Motor Insurance Extreme Claims through Appropriate Statistical Distributions

In the area of insurance, probability modeling has a wide variety of applications. In life insurance, the compensation sum is calculated in advance and may often be estimated using actuarial techniques, while in motor insurance, the claim amount is generally not known in advance. In the insurance business, the improvement of actuarial risk control strategies is an essential technique for controlling insurance risk. Although an insurance company’s risk assessment about its solvency is a complex and detailed problem, its solution begins with statistical modeling of individual claims’ amounts. This article emphasizes the possible ways of obtaining a suitable probability distribution model that accurately explains insurance risks and how to use such a model for risk management purposes. For this reason, we have applied modern programming techniques and statistical software implemented the methods provided based on data on premium amounts of third-party motor insurance claims.

Diameter distribution model development of tropical hybrid Eucalyptus clonal plantations in Sumatera, Indonesia: A comparison of estimation methods

Background: Effective forest management and planning often requires information about the distribution of volume by size and product classes. Size-class models describe the diameter distribution and provide information by diameter class, such as the number of trees, basal area, and volume per unit of area. A successful diameter-distribution model requires high flexibility yet robust prediction of its parameters. To our knowledge, there are no studies regarding diameter distribution models for Eucalyptus hybrids in Indonesia. Therefore, the aim of this study was to compare different recovery methods for predicting parameters of the 3-parameter Weibull distribution for characterising diameter distributions of Eucalyptus hybrid clone plantations, on Sumatera Island of Indonesia. Methods: The parameter recovery approach was proposed to be compatible with stand-average growth and yield models developed based on the same data. Three approaches where compared: moment-based recovery, percentile-based prediction and hybrid methods. The ultimate goal was to recover Weibull parameters from future stand attributes, which were predicted from current stand attributes using regression models. Results: In this study, the moment method was found to give the overall lowest mean error-index and Kolmogorov– Smirnov (KS) statistic, followed by the hybrid and percentile methods. The moment-based method better fit long tails on both sides of the distribution and exhibited slightly greater flexibility in describing plots with larger variance than the other methods. Conclusions: The Weibull approach appeared relatively robust in determining diameter distributions of Eucalyptus hybrid clone plantation in Indonesia, yet some refinements may be necessary to characterize more complex distributions.

Study on Distribution Characteristics of Metro Stray Current and Evaluation of Cumulative Corrosion Effect

Stray current directly affects the regular operation of electrical equipment and facilities in the subway DC traction power supply system. Therefore, it is worthwhile to study the stray current distribution characteristics during train operation and the quantitative corrosion of buried pipelines. This paper introduces the traction characteristics of power carriages and power wheelsets of subway vehicles into the DC traction process. A finite element model considering the dynamic distribution of stray current under the actual operation of subway vehicles is established. The interference characteristics of stray current and the contribution of power sources under the multiparticle model are analyzed. The rail insulation damage caused by long service time and the quantitative calculation of rail and buried pipeline corrosion is considered. The model results show that the stray current in the buried pipeline under the multiparticle model is more accurate and more suitable for the protection in the actual subway. The quantitative corrosion of the buried pipeline is stronger than the partial insulation damage environment when the rail is not insulated. The rail and buried pipeline corrosion at both ends of the insulation damage position is relatively severe. The stray current distribution model established in this paper gives full play to the solution advantages of the finite element method and provides a new idea for the quantitative calculation of buried pipeline corrosion.

Stress-Testing Framework for Urban Water Systems: A Source to Tap Approach for Stochastic Resilience Assessment

Optimizing the design and operation of an Urban Water System (UWS) faces significant challenges over its lifespan to account for the uncertainties of important stressors that arise from population growth rates, climate change factors, or shifting demand patterns. The analysis of a UWS’s performance across interdependent subsystems benefits from a multi-model approach where different designs are tested against a variety of metrics and in different times scales for each subsystem. In this work, we present a stress-testing framework for UWSs that assesses the system’s resilience, i.e., the degree to which a UWS continues to perform under progressively increasing disturbance (deviation from normal operating conditions). The framework is underpinned by a modeling chain that covers the entire water cycle, in a source-to-tap manner, coupling a water resources management model, a hydraulic water distribution model, and a water demand generation model. An additional stochastic simulation module enables the representation and modeling of uncertainty throughout the water cycle. We demonstrate the framework by “stress-testing” a synthetic UWS case study with an ensemble of scenarios whose parameters are stochastically changing within the UWS simulation timeframe and quantify the uncertainty in the estimation of the system’s resilience.

Component-based Access Design Method for Dynamic Spatial Database

In order to realize the optimal access of dynamic spatial database, a component-based optimal access method of dynamic spatial database is proposed. The statistical information distribution model for storing the characteristic data of association rules is constructed in the dynamic spatial database. The fuzzy information features are extracted by using the dynamic component fusion clustering analysis method. Combined with the distributed association feature quantity, the fusion scheduling is carried out to control the dynamic information clustering. Combined with fuzzy c-means clustering analysis method, dynamic attribute classification analysis is carried out. The dynamic component block matching model is used for update iterative optimization, and the optimal access to the dynamic spatial database is realized in the cluster center. Simulation results show that this method has strong adaptability to the optimal access of dynamic spatial database, and has high accuracy and good convergence for data information extraction in dynamic spatial database.

Jointly modeling marine species to inform the effects of environmental change on an ecological community in the Northwest Atlantic

Single species distribution models (SSDMs) are typically used to understand and predict the distribution and abundance of marine fish by fitting distribution models for each species independently to a combination of abiotic environmental variables. However, species abundances and distributions are influenced by abiotic environmental preferences as well as biotic dependencies such as interspecific competition and predation. When species interact, a joint species distribution model (JSDM) will allow for valid inference of environmental effects. We built a joint species distribution model of marine fish and invertebrates of the Northeast US Continental Shelf, providing evidence on species relationships with the environment as well as the likelihood of species to covary. Predictive performance is similar to SSDMs but the Bayesian joint modeling approach provides two main advantages over single species modeling: (1) the JSDM directly estimates the significance of environmental effects; and (2) predicted species richness accounts for species dependencies. An additional value of JSDMs is that the conditional prediction of species distributions can use not only the environmental associations of species, but also the presence and abundance of other species when forecasting future climatic associations.