Fast Direct Torque Control of an Open-End Induction Motor Drive Using 12-Sided Polygonal Voltage Space Vectors

A torque control scheme, based on a direct torque control (DTC) algorithm using a 12-sided polygonal voltage space vector, is proposed for a variable speed control of an open-end induction motor drive. The conventional DTC scheme uses a stator flux vector for the sector identification and then the switching vector to control stator flux and torque. However, the proposed DTC scheme selects switching vectors based on the sector information of the estimated fundamental stator voltage vector and its relative position with respect to the stator flux vector. The fundamental stator voltage estimation is based on the steady-state model of IM and the synchronous frequency of operation is derived from the computed stator flux using a low-pass filter technique. The proposed DTC scheme utilizes the exact positions of the fundamental stator voltage vector and stator flux vector to select the optimal switching vector for fast control of torque with small variation of stator flux within the hysteresis band. The present DTC scheme allows full load torque control with fast transient response to very low speeds of operation, with reduced switching frequency variation. Extensive experimental results are presented to show the fast torque control for speed of operation from zero to rated.

Null Dereference Verification via Over-approximated Weakest Pre-conditions Analysis

Null dereferences are a bane of programming in languages such as Java. In this paper we propose a sound, demand-driven, inter-procedurally context-sensitive dataflow analysis technique to verify a given dereference as safe or potentially unsafe. Our analysis uses an abstract lattice of formulas to find a pre-condition at the entry of the program such that a null-dereference can occur only if the initial state of the program satisfies this pre-condition. We use a simplified domain of formulas, abstracting out integer arithmetic, as well as unbounded access paths due to recursive data structures. For the sake of precision we model aliasing relationships explicitly in our abstract lattice, enable strong updates, and use a limited notion of path sensitivity. For the sake of scalability we prune formulas continually as they get propagated, reducing to true conjuncts that are less likely to be useful in validating or invalidating the formula. We have implemented our approach, and present an evaluation of it on a set of ten real Java programs. Our results show that the set of design features we have incorporated enable the analysis to (a) explore long, inter-procedural paths to verify each dereference, with (b) reasonable accuracy, and (c) very quick response time per dereference, making it suitable for use in desktop development environments.

Chemistry and biology of DNA-binding small molecules

Regulation of the transcription machinery is one of the many ways to achieve control of gene expression. This has been done either at the transcription initiation stage or at the elongation stage. Different methodologies are known to inhibit transcription initiation via targeting of double-stranded (ds) DNA by: (i) synthetic oligonucleotides, (ii) ds-DNA-specific, sequenceselective minor-groove binders (distamycin A), intercalators (daunomycin) combilexins and (iii) small molecule (peptide or intercalator)-oligonucleotide conjugates. In some cases, instead of ds-DNA, higher order G-quadruplex structures are formed at the start site of transcription. In this regard G-quadruplex DNA-specific small molecules play a significant role towards inhibition of the transcription machinery. Different types of designer DNA-binding agents act as powerful sequence-specific gene modulators, by exerting their effect from transcription regulation to gene modification. But most of these chemotherapeutic agents have serious side effects. Accordingly, there is always a challenge to design such DNA-binding molecules that should not only achieve maximum specific DNA-binding affinity, and cellular and nuclear transport activity, but also would not interfere with the functions of normal cells.

Network Reconstruction from dynamic data

Over the past decade, many powerful data mining techniques have been developed to analyze temporal and sequential data. The time is now fertile for addressing problems of larger scope under the purview of temporal data mining. The fourth SIGKDD workshop on temporal data mining focused on the question: What can we infer about the structure of a complex dynamical system from observed temporal data? The goals of the workshop were to critically evaluate the need in this area by bringing together leading researchers from industry and academia, and to identify promising technologies and methodologies for doing the same. We provide a brief summary of the workshop proceedings and ideas arising out of the discussions.

Microprocessor-based algorithm for symmetrical-components calculation

This letter presents a microprocessor-based algorithm for calculating symmetrical components from the distorted transient voltage and current signals in a power system. The fundamental frequency components of the 3-phase signals are first extracted using an algorithm based on Haar functions and then "symmetrical-component transformation is applied to obtain the sequence components. The algorithm presented is computationally efficient and fast. This algorithm is better suited for application in microprocessor-based protection schemes of synchronous and induction machines.

Delay Optimal Event Detection on Ad Hoc Wireless Sensor Networks

We consider a small extent sensor network for event detection, in which nodes periodically take samples and then contend over a random access network to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center for processing the measurements. The Bayesian setting, is assumed, that is, the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is network-oblivious and makes a decision only when a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is network-aware and processes measurements as they arrive, but in a time-causal order. In this case, the decision statistic depends on the network delays, whereas in the network-oblivious case, the decision statistic does not. This yields a Bayesian change-detection problem with a trade-off between the random network delay and the decision delay that is, a higher sampling rate reduces the decision delay but increases the random access delay. Under periodic sampling, in the network-oblivious case, the structure of the optimal stopping rule is the same as that without the network, and the optimal change detection delay decouples into the network delay and the optimal decision delay without the network. In the network-aware case, the optimal stopping problem is analyzed as a partially observable Markov decision process, in which the states of the queues and delays in the network need to be maintained. A sufficient decision statistic is the network state and the posterior probability of change having occurred, given the measurements received and the state of the network. The optimal regimes are studied using simulation.

Enhanced J-protein interaction and compromised protein stability of mtHsp70 variants lead to mitochondrial dysfunction in Parkinsons disease

Parkinsons disease (PD) is the second most prevalent progressive neurological disorder commonly associated with impaired mitochondrial function in dopaminergic neurons. Although familial PD is multifactorial in nature, a recent genetic screen involving PD patients identified two mitochondrial Hsp70 variants (P509S and R126W) that are suggested in PD pathogenesis. However, molecular mechanisms underlying how mtHsp70 PD variants are centrally involved in PD progression is totally elusive. In this article, we provide mechanistic insights into the mitochondrial dysfunction associated with human mtHsp70 PD variants. Biochemically, the R126W variant showed severely compromised protein stability and was found highly susceptible to aggregation at physiological conditions. Strikingly, on the other hand, the P509S variant exhibits significantly enhanced interaction with J-protein cochaperones involved in folding and import machinery, thus altering the overall regulation of chaperone-mediated folding cycle and protein homeostasis. To assess the impact of mtHsp70 PD mutations at the cellular level, we developed yeast as a model system by making analogous mutations in Ssc1 ortholog. Interestingly, PD mutations in yeast (R103W and P486S) exhibit multiple in vivo phenotypes, which are associated with omitochondrial dysfunction', including compromised growth, impairment in protein translocation, reduced functional mitochondrial mass, mitochondrial DNA loss, respiratory incompetency and increased susceptibility to oxidative stress. In addition to that, R103W protein is prone to aggregate in vivo due to reduced stability, whereas P486S showed enhanced interaction with J-proteins, thus remarkably recapitulating the cellular defects that are observed in human PD variants. Taken together, our findings provide evidence in favor of direct involvement of mtHsp70 as a susceptibility factor in PD.

Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks

Lack of supervision in clustering algorithms often leads to clusters that are not useful or interesting to human reviewers. We investigate if supervision can be automatically transferred for clustering a target task, by providing a relevant supervised partitioning of a dataset from a different source task. The target clustering is made more meaningful for the human user by trading-off intrinsic clustering goodness on the target task for alignment with relevant supervised partitions in the source task, wherever possible. We propose a cross-guided clustering algorithm that builds on traditional k-means by aligning the target clusters with source partitions. The alignment process makes use of a cross-task similarity measure that discovers hidden relationships across tasks. When the source and target tasks correspond to different domains with potentially different vocabularies, we propose a projection approach using pivot vocabularies for the cross-domain similarity measure. Using multiple real-world and synthetic datasets, we show that our approach improves clustering accuracy significantly over traditional k-means and state-of-the-art semi-supervised clustering baselines, over a wide range of data characteristics and parameter settings.

Theory and Algorithms for Hop-Count-Based Localization with Random Geometric Graph Models of Dense Sensor Networks

Wireless sensor networks can often be viewed in terms of a uniform deployment of a large number of nodes in a region of Euclidean space. Following deployment, the nodes self-organize into a mesh topology with a key aspect being self-localization. Having obtained a mesh topology in a dense, homogeneous deployment, a frequently used approximation is to take the hop distance between nodes to be proportional to the Euclidean distance between them. In this work, we analyze this approximation through two complementary analyses. We assume that the mesh topology is a random geometric graph on the nodes; and that some nodes are designated as anchors with known locations. First, we obtain high probability bounds on the Euclidean distances of all nodes that are h hops away from a fixed anchor node. In the second analysis, we provide a heuristic argument that leads to a direct approximation for the density function of the Euclidean distance between two nodes that are separated by a hop distance h. This approximation is shown, through simulation, to very closely match the true density function. Localization algorithms that draw upon the preceding analyses are then proposed and shown to perform better than some of the well-known algorithms present in the literature. Belief-propagation-based message-passing is then used to further enhance the performance of the proposed localization algorithms. To our knowledge, this is the first usage of message-passing for hop-count-based self-localization.

A study of epileptogenic network structures in rat hippocampal cultures using first spike latencies during synchronization events

Study of hypersynchronous activity is of prime importance for combating epilepsy. Studies on network structure typically reconstruct the network by measuring various aspects of the interaction between neurons and subsequently measure the properties of the reconstructed network. In sub-sampled networks such methods lead to significant errors in reconstruction. Using rat hippocampal neurons cultured on a multi-electrode array dish and a glutamate injury model of epilepsy in vitro, we studied synchronous activity in neuronal networks. Using the first spike latencies in various neurons during a network burst, we extract various recurring spatio-temporal onset patterns in the networks. Comparing the patterns seen in control and injured networks, we observe that injured networks express a wide diversity in their foci (origin) and activation pattern, while control networks show limited diversity. Furthermore, we note that onset patterns in glutamate injured networks show a positive correlation between synchronization delay and physical distance between neurons, while control networks do not.

A study of epileptogenic network structures in rat hippocampal cultures using first spike latencies during synchronization events

Study of hypersynchronous activity is of prime importance for combating epilepsy. Studies on network structure typically reconstruct the network by measuring various aspects of the interaction between neurons and subsequently measure the properties of the reconstructed network. In sub-sampled networks such methods lead to significant errors in reconstruction. Using rat hippocampal neurons cultured on a multi-electrode array dish and a glutamate injury model of epilepsy in vitro, we studied synchronous activity in neuronal networks. Using the first spike latencies in various neurons during a network burst, we extract various recurring spatio-temporal onset patterns in the networks. Comparing the patterns seen in control and injured networks, we observe that injured networks express a wide diversity in their foci (origin) and activation pattern, while control networks show limited diversity. Furthermore, we note that onset patterns in glutamate injured networks show a positive correlation between synchronization delay and physical distance between neurons, while control networks do not.

Grafting of Dispersants on MoS2 Nanoparticles in Base Oil Lubrication of Steel

Solid lubricant nanoparticles in suspension in oil are good lubricating options for practical machinery. In this article, we select a range of dispersants, based on their polar moieties, to suspend 50-nm molybdenum disulfide particles in an industrial base oil. The suspension is used to lubricate a steel on steel sliding contact. A nitrogen-based polymeric dispersant (aminopropyl trimethoxy silane) with a free amine group and an oxygen-based polymeric dispersant (sorbital monooleate) when grafted on the particle charge the particle negatively and yield an agglomerate size which is almost the same as that of the original particle. Lubrication of the contact by these suspensions gives a coefficient of friction in the similar to 0.03 range. The grafting of these surfactants on the particle is shown here to be of a chemical nature and strong as the grafts survive mechanical shear stress in tribology. Such grafts are superior to those of other silane-based test surfactants which have weak functional groups. In the latter case, the particles bereft of strong grafts agglomerate easily in the lubricant and give a coefficient of friction in the 0.08-0.12 range. This article investigates the mechanism of frictional energy dissipation as influenced by the chemistry of the surfactant molecule.

Exploiting the Structure of the Constraint Graph for Efficient Points-to Analysis

Points-to analysis is a key compiler analysis. Several memory related optimizations use points-to information to improve their effectiveness. Points-to analysis is performed by building a constraint graph of pointer variables and dynamically updating it to propagate more and more points-to information across its subset edges. So far, the structure of the constraint graph has been only trivially exploited for efficient propagation of information, e.g., in identifying cyclic components or to propagate information in topological order. We perform a careful study of its structure and propose a new inclusion-based flow-insensitive context-sensitive points-to analysis algorithm based on the notion of dominant pointers. We also propose a new kind of pointer-equivalence based on dominant pointers which provides significantly more opportunities for reducing the number of pointers tracked during the analysis. Based on this hitherto unexplored form of pointer-equivalence, we develop a new context-sensitive flow-insensitive points-to analysis algorithm which uses incremental dominator update to efficiently compute points-to information. Using a large suite of programs consisting of SPEC 2000 benchmarks and five large open source programs we show that our points-to analysis is 88% faster than BDD-based Lazy Cycle Detection and 2x faster than Deep Propagation. We argue that our approach of detecting dominator-based pointer-equivalence is a key to improve points-to analysis efficiency.

Two-Dimensional Correlation Analysis of Temperature-Dependent FT-IR Spectra of Oleic Acid

In the present study, variable temperature FT-IR spectroscopic investigations were used to characterize the spectral changes in oleic acid during heating oleic acid in the temperature range from -30 degrees;C to 22 degrees C. In order to extract more information about the spectral variations taking place during the phase transition process, 2D correlation spectroscopy (2DCOS) was employed for the stretching (C?O) and rocking (CH2) band of oleic acid. However, the interpretation of these spectral variations in the FT-IR spectra is not straightforward, because the absorption bands are heavily overlapped and change due to two processes: recrystallization of the ?-phase and melting of the oleic acid. Furthermore, the solid phase transition from the ?- to the a-phase was also observed between -4 degrees C and -2 degrees C. Thus, for a more detailed 2DCOS analysis, we have split up the spectral data set in the subsets recorded between -30 degrees C to -16 degrees C, -16 degrees C to 10 degrees C, and 10 degrees C to 22 degrees C. In the corresponding synchronous and asynchronous 2D correlation plots, absorption bands that are characteristic of the crystalline and amorphous regions of oleic acid were separated.

Incentive Compatible Influence Maximization in Social Networks with Application to Viral Marketing

Information diffusion and influence maximization are important and extensively studied problems in social networks. Various models and algorithms have been proposed in the literature in the context of the influence maximization problem. A crucial assumption in all these studies is that the influence probabilities are known to the social planner. This assumption is unrealistic since the influence probabilities are usually private information of the individual agents and strategic agents may not reveal them truthfully. Moreover, the influence probabilities could vary significantly with the type of the information flowing in the network and the time at which the information is propagating in the network. In this paper, we use a mechanism design approach to elicit influence probabilities truthfully from the agents. Our main contribution is to design a scoring rule based mechanism in the context of the influencer-influencee model. In particular, we show the incentive compatibility of the mechanisms and propose a reverse weighted scoring rule based mechanism as an appropriate mechanism to use.