An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data

Neural Distributed Autoassociative Memories: A Survey

Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension. The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons). Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints. Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors.Comment: 31 page

Analisis perbandingan Algoritma Dynamic Programming dengan pendekatan Forward dan Backward melalui hasil studi kasus distribusi produk air minum kemasan galon di depot air minum isi ulang Banyu Belik, Purwokerto

Dynamic Programming (DP) merupakan salah satu algoritma optimasi yang dapat diaplikasikan dalam kehidupan sehari-hari. Dynamic Programming menguraikan solusi menjadi tahapan-tahapan sehingga permasalahan dapat dipandang melalui serangkaian keputusan yang saling berkaitan. Dalam menyelesaikan permasalahan, Dynamic Programming memiliki dua pendekatan yaitu Forward dan Backward. Dengan kondisi yang sama, suatu permasalahan dapat diselesaikan melalui dua pendekatan tersebut. Namun pada penerapannya proses pencapaian nilai optimal pada tiap stage antara pendekatan Forward dan Backward ialah berbeda meskipun dengan nilai optimal akhir yang sama. Pada kondisi yang sama juga suatu permasalahan dapat menghasilkan nilai optimal akhir yang berbeda jika diselesaikan dengan pendekatan Forward dan Backward. Terkait dengan hal tersebut, maka Tugas Akhir ini bertujuan untuk mengetahui apa saja faktor yang mempengaruhi perbedaan tersebut serta mengetahui karakteristik dari tiap pendekatan diatas. Dalam penelitian ini digunakan data dari studi kasus distribusi produk air kemasan galon di depot air minum isi ulang Banyu Belik yang terdapat di daerah Purwokerto. Pada studi kasus ini sebelumnya telah dilakukan optimasi dengan menggunakan kombinasi algoritma genetika dan pencarian tabu. Sehingga selain Melalui Tugas Akhir ini juga akan dilakukan perbandingan antara penyelesaian dengan menggunakan Dynamic Programming (DP) dan kombinasi algoritma genetika dan pencarian tabu untuk mengetahui hasil mana yang lebih optimal. Hasil dari tugas akhir ini menunjukkan bahwa pendekatan forward dan backward menghasilkan nilai optimal yang berbeda. Perbedaan nilai optimal tersebut dikarenakan oleh karakteristik dynamic programming dimana nilai optimum pada stage -k akan dipengaruhi oleh nilai optimum pada stage k-1. Selain itu faktor lain yang mengakibatkan adanya perbedaan nilai optimal untuk masing-masing pendekatan adalah karena karakteristik permasalahan Travelling Salesman Problem (TSP) yang memiliki beberapa titik tujuan yang dinamis. Permasalahan studi kasus berfokus kepada penyusunan rangkaian node agar mencapai nilai yang optimal. ============================================================================= Dynamic Programming (DP) is one of the optimization algorithms can be applied in daily life. Dynamic Programming devides solution into stages so that the problem can be seen through a series of interrelated decisions. By solving a problem, Dynamic Programming has two approaches, Forward and Backward. In the same conditions, a problem can be solved through two approaches. However, the implementation process of achieving optimal value at each stage between Forward and Backward approach is different even with the same final optimum value. On certain condition, a problem can also generate different final optimal value if completed with Forward and Backward approach. Related to this case, the final project aims to find out what factors influence these differences and to know the characteristics of both approaches. This study used data from a case study of packaged water distribution at water refill depot Banyu Belik contained in Purwokerto. In this case study had previously been done optimization by using a combination of genetic algorithm and tabu search. So in addition Through this Final will also be made a comparison between Dynamic Programming (DP) and the combination of genetic algorithm and tabu search to find out which is more optimal results. The results of this thesis show that the forward and backward approach produces different optimal values. The optimal rate differences due to the characteristics of dynamic programming on a stage where the optimum value of k will be influenced by the optimum value in stage k-1. Besides other factors that lead to differences in the optimal value for each approach is due to the characteristics of the problem Travelling Salesman Problem (TSP), which has several points of interest are dynamic. The problems of case studies focused on the preparation of a series of nodes in order to achieve optimal valu

Plant Modeling, Model Reduction and Power Optimization for an Organic Rankine Cycle Waste Heat Recovery System in Heavy Duty Diesel Engine Applications

With pressure from strict emission and fuel consumption regulations, researchers are searching for improved internal combustion engine performance. Especially for the heavy-duty vehicles, which takes up 7% of the total vehicle volume while consume around 30% of transportation energy in US. Around 40-60% of energy is wasted as heat in heavy-duty diesel (HDD) vehicles in different engine operating conditions, which mainly includes the waste heat in exhaust gas, exhaust gas recirculation (EGR) circuit, and engine coolant. Waste heat recovery (WHR) techniques are potential to achieve the fuel economy and emission reduction goals. Among the available WHR techniques, organic Rankine cycle (ORC) is preferred by many researchers for its mature technologies and high efficiency. The aim of this dissertation is to analyze the power of HDD vehicle by: (i) building a high fidelity, physics-based ORC-WHR dynamic system plant model, (ii) building a reduced order model framework, and (iii) conducting the power analysis based on the developed plant and reduced models. The dynamic system plant model is built, which includes heat exchangers, a turbine expander, pumps, control valves, compressible volumes, junctions and a reservoir. Components are modelled and calibrated individually. Subsequently, the component models are integrated into an entire ORC-WHR system model. The entire ORC-WHR system model is validated over transient engine conditions. Actuator sensitivity study is conducted for the ORC-WHR power generation analysis using the ORC-WHR plant model. Besides the ORC-WHR plant model, a reduced order model framework is developed utilizing Proper Orthogonal Decomposition (POD) and Galerkin projection approaches. The POD-Galerkin reduced order model framework inherits the system physics from the high fidelity, physics-based ORC-WHR plant model. POD Galerkin reduced order models are compared with three existing models (finite volume model, moving boundary model and 0D lumped model) and show their advantages over the existing models in terms of accuracy or computation cost. In addition, identification method is applied to the low order POD Galerkin reduced order model to increase the accuracy. Given the validated ORC-WHR plant model and POD Galerkin reduced order model framework, the ORC-WHR system power analysis is conducted. Steady state power analysis is conducted over two quasi-steady driving cycles using the ORC-WHR plant model. An engine model is developed to predict the exhaust conditions in transient engine operating conditions. Transient power analysis is conducted with ORC-WHR plant model and engine model co-simulation by optimizing three vapor temperature reference trajectories. Finally, dynamic programming (DP) is implemented with the POD-Galerkin reduced order model to generate ORC-WHR power benchmark in a driving cycle, which can give the guidance on the ORC power optimization and evaluate the controller performance