11,447 research outputs found
The looping probability of random heteropolymers helps to understand the scaling properties of biopolymers
Random heteropolymers are a minimal description of biopolymers and can
provide a theoretical framework to the investigate the formation of loops in
biophysical experiments. A two--state model provides a consistent and robust
way to study the scaling properties of loop formation in polymers of the size
of typical biological systems. Combining it with self--adjusting
simulated--tempering simulations, we can calculate numerically the looping
properties of several realizations of the random interactions within the chain.
Differently from homopolymers, random heteropolymers display at different
temperatures a continuous set of scaling exponents. The necessity of using
self--averaging quantities makes finite--size effects dominant at low
temperatures even for long polymers, shadowing the length--independent
character of looping probability expected in analogy with homopolymeric
globules. This could provide a simple explanation for the small scaling
exponents found in experiments, for example in chromosome folding
Orthogonal learning particle swarm optimization
Particle swarm optimization (PSO) relies on its
learning strategy to guide its search direction. Traditionally,
each particle utilizes its historical best experience and its neighborhood’s
best experience through linear summation. Such a
learning strategy is easy to use, but is inefficient when searching
in complex problem spaces. Hence, designing learning strategies
that can utilize previous search information (experience) more
efficiently has become one of the most salient and active PSO
research topics. In this paper, we proposes an orthogonal learning
(OL) strategy for PSO to discover more useful information that
lies in the above two experiences via orthogonal experimental
design. We name this PSO as orthogonal learning particle swarm
optimization (OLPSO). The OL strategy can guide particles to
fly in better directions by constructing a much promising and
efficient exemplar. The OL strategy can be applied to PSO with
any topological structure. In this paper, it is applied to both global
and local versions of PSO, yielding the OLPSO-G and OLPSOL
algorithms, respectively. This new learning strategy and the
new algorithms are tested on a set of 16 benchmark functions, and
are compared with other PSO algorithms and some state of the
art evolutionary algorithms. The experimental results illustrate
the effectiveness and efficiency of the proposed learning strategy
and algorithms. The comparisons show that OLPSO significantly
improves the performance of PSO, offering faster global convergence,
higher solution quality, and stronger robustness
Compressed Air Energy Storage-Part I: An Accurate Bi-linear Cavern Model
Compressed air energy storage (CAES) is suitable for large-scale energy
storage and can help to increase the penetration of wind power in power
systems. A CAES plant consists of compressors, expanders, caverns, and a
motor/generator set. Currently used cavern models for CAES are either accurate
but highly non-linear or linear but inaccurate. Highly non-linear cavern models
cannot be directly utilized in power system optimization problems. In this
regard, an accurate bi-linear cavern model for CAES is proposed in this first
paper of a two-part series. The charging and discharging processes in a cavern
are divided into several virtual states and then the first law of
thermodynamics and ideal gas law are used to derive a cavern model, i.e., model
for the variation of temperature and pressure in these processes. Thereafter,
the heat transfer between the air in the cavern and the cavern wall is
considered and integrated into the cavern model. By subsequently eliminating
several negligible terms, the cavern model reduces to a bi-linear (linear)
model for CAES with multiple (single) time steps. The accuracy of the proposed
cavern model is verified via comparison with an accurate non-linear model.Comment: 8 page
Theta frequency prefrontal–hippocampal driving relationship during free exploration in mice
AbstractInter-connected brain areas coordinate to process information and synchronized neural activities engage in learning and memory processes. Recent electrophysiological studies in rodents have implicated hippocampal–prefrontal connectivity in anxiety, spatial learning and memory-related tasks. In human patients with schizophrenia and autism, robust reduced connectivity between the hippocampus (HPC) and prefrontal cortex (PFC) has been reported. However little is known about the directionality of these oscillations and their roles during active behaviors remain unclear. Here the directional information processing in mice was measured by Granger causality, a mathematical tool that has been used in neuroscience to quantify the oscillatory driving relationship between the ventral HPC (vHPC) and the PFC in two anxiety tests and between the dorsal HPC (dHPC) and the PFC in social interaction test. In the open field test, stronger vHPC driving to the PFC was found in the center compartment than in the wall area. In the light–dark box test, PFC to vHPC causality was higher than vHPC to PFC causality although no difference was found between the light and dark areas for the causality in both directions. In the social interaction test using Cx3cr1 knockout mice which model for deficient microglia-dependent synaptic pruning, higher PFC driving to the dHPC was found than driving from the dHPC to the PFC in both knockout mice and wild-type mice. Cx3cr1 knockout mice showed reduced baseline PFC driving to the dHPC compared to their wild-type littermates. PFC to dHPC causality could predict the actual time spent interacting with the social stimuli. The current findings indicate that directed oscillatory activities between the PFC and the HPC have task-dependent roles during exploration in the anxiety test and in the social interaction test
Compressed Air Energy Storage-Part II: Application to Power System Unit Commitment
Unit commitment (UC) is one of the most important power system operation
problems. To integrate higher penetration of wind power into power systems,
more compressed air energy storage (CAES) plants are being built. Existing
cavern models for the CAES used in power system optimization problems are not
accurate, which may lead to infeasible solutions, e.g., the air pressure in the
cavern is outside its operating range. In this regard, an accurate CAES model
is proposed for the UC problem based on the accurate bi-linear cavern model
proposed in the first paper of this two-part series. The minimum switch time
between the charging and discharging processes of CAES is considered. The whole
model, i.e., the UC model with an accurate CAES model, is a large-scale mixed
integer bi-linear programming problem. To reduce the complexity of the whole
model, three strategies are proposed to reduce the number of bi-linear terms
without sacrificing accuracy. McCormick relaxation and piecewise linearization
are then used to linearize the whole model. To decrease the solution time, a
method to obtain an initial solution of the linearized model is proposed. A
modified RTS-79 system is used to verify the effectiveness of the whole model
and the solution methodology.Comment: 8 page
Optimal Attack against Cyber-Physical Control Systems with Reactive Attack Mitigation
This paper studies the performance and resilience of a cyber-physical control
system (CPCS) with attack detection and reactive attack mitigation. It
addresses the problem of deriving an optimal sequence of false data injection
attacks that maximizes the state estimation error of the system. The results
provide basic understanding about the limit of the attack impact. The design of
the optimal attack is based on a Markov decision process (MDP) formulation,
which is solved efficiently using the value iteration method. Using the
proposed framework, we quantify the effect of false positives and
mis-detections on the system performance, which can help the joint design of
the attack detection and mitigation. To demonstrate the use of the proposed
framework in a real-world CPCS, we consider the voltage control system of power
grids, and run extensive simulations using PowerWorld, a high-fidelity power
system simulator, to validate our analysis. The results show that by carefully
designing the attack sequence using our proposed approach, the attacker can
cause a large deviation of the bus voltages from the desired setpoint. Further,
the results verify the optimality of the derived attack sequence and show that,
to cause maximum impact, the attacker must carefully craft his attack to strike
a balance between the attack magnitude and stealthiness, due to the
simultaneous presence of attack detection and mitigation
Modeling and Detecting False Data Injection Attacks against Railway Traction Power Systems
Modern urban railways extensively use computerized sensing and control
technologies to achieve safe, reliable, and well-timed operations. However, the
use of these technologies may provide a convenient leverage to cyber-attackers
who have bypassed the air gaps and aim at causing safety incidents and service
disruptions. In this paper, we study false data injection (FDI) attacks against
railways' traction power systems (TPSes). Specifically, we analyze two types of
FDI attacks on the train-borne voltage, current, and position sensor
measurements - which we call efficiency attack and safety attack -- that (i)
maximize the system's total power consumption and (ii) mislead trains' local
voltages to exceed given safety-critical thresholds, respectively. To
counteract, we develop a global attack detection (GAD) system that serializes a
bad data detector and a novel secondary attack detector designed based on
unique TPS characteristics. With intact position data of trains, our detection
system can effectively detect the FDI attacks on trains' voltage and current
measurements even if the attacker has full and accurate knowledge of the TPS,
attack detection, and real-time system state. In particular, the GAD system
features an adaptive mechanism that ensures low false positive and negative
rates in detecting the attacks under noisy system measurements. Extensive
simulations driven by realistic running profiles of trains verify that a TPS
setup is vulnerable to the FDI attacks, but these attacks can be detected
effectively by the proposed GAD while ensuring a low false positive rate.Comment: IEEE/IFIP DSN-2016 and ACM Trans. on Cyber-Physical System
Rotor field orientation speed and torque control of BDFM with adaptive second order sliding mode
This paper presents two cascaded second order sliding mode controllers (SOSMCs) for brushless doubly fed motor (BDFM) adjustable speed system, which regulate the speed and torque. And an adaptive super twisting algorithm is incorporated into the SOSMCs to adaptively regulate the law of SOSMC. The proposed controllers for BDFM eliminate the average chattering encountered by most sliding mode control (SMC) schemes, and also possess the robustness and excellent static and dynamic performances of SMC. Simulation results show that the proposed control strategy is feasible, proper and effective. © 2013 IEEE
- …