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A crucial problem is the risk that a manipulator arm would be damaged by twisting or bending during and after contacting a target satellite. This paper presents a solution to minimize the risk of damage to the arm and thereby enhance contact performance. First, a hand-eye servo controller is proposed as a method for accurately tracking and capturing a target satellite. Next, a motion planning strategy is employed to obtain the best-fit contacting moments. Also, an impedance control law is implemented to increase protection during operation and to ensure more accurate compliance. Finally, to overcome the challenge of verifying algorithms for a space manipulator while on the ground, a novel experimental system with a 6-DOF (degree of freedom) manipulator on a chaser field robot is presented and implemented to capture a target field robot; the proposed methods are then validated using the experimental platform.
Unimodality and strong unimodality of the distribution of ascendingly ordered random variables have been extensively studied in the literature, whereas these properties have not received much attention in the case of descendingly ordered random variates. In this paper, we show that log concavity of the reversed hazard rate implies that of the density function. Using this fundamental result, we establish some convexity properties of such random variables. To do this, we first provide a counterexample showing that a claim of Basak & Basak [7] about the lower record values is not valid. Then, we provide conditions under which unimodality properties of the distribution of lower k-record values would hold. Finally, some extensions to dual generalized order statistics in both univariate and multivariate cases are discussed.
The mathematician Claude Shannon first formalized the notion of perfect secrecy and showed that certain cryptosystems realized it. We do not cover all of his theory, but address the part that is directly relevant to our study of cryptography and that relies on math that is within the scope of the text.
In this chapter, we discuss how to tell whether a cryptosystem is perfectly secure. In Chapter 7, we discuss some more ways to use perfect secrecy. It should become clear to the reader why modular arithmetic is used instead of ordinary arithmetic for much of cryptography.
As we will see, perfect secrecy of a cryptosystem and unique decryptability are mathematical cousins. A cryptosystem may be uniquely decryptable and not perfectly secure, or vice versa. However, the math that goes into determining perfect secrecy is very similar to the math that goes into determining unique decryptability.
What does an eavesdropper learn from seeing a cyphertext?
A cryptosystem is perfectly secure if an eavesdropper learns nothing about the plaintext from seeing the cyphertext. To understand what cryptosystems are secure, therefore, we consider what it means to learn something.
For this purpose, we consider a very simple scenario. Alice sends Bob an encrypted message, and Eve intercepts the cyphertext. (For now, we ignore the possibility that Bob may respond using the same cryptosystem and even the same key.) To understand what Eve has learned from seeing the cyphertext, we consider her knowledge of the plaintext before (her a priori knowledge) and after she sees the cyphertext (her a posteriori knowledge).
Consider an experiment that depends on chance, and that does not depend on any unknown information. What can we say about the outcome? If we actually carry the experiment out, we can say precisely what happened – but what can we say beforehand? The best description we can hope to give is one that specifies the likelihood of each possible outcome. Such a description is called a probability distribution. Probability theory is a way of reasoning about likelihoods.
Consider the roll of a die – there are six possible outcomes (not including wacky things like “the die rolls right off the table”). We implicitly assume that outcomes of a single experiment are mutually exclusive. That is, only one of the outcomes can occur each time that the experiment is performed.
The possible outcomes are shown in Figure 5.1.
The set of possible outcomes is called the sample space. It is also called the probability space.
Probabilities of outcomes
We describe the relative likelihoods of the six possible outcomes by assigning each outcome a number that represents its probability. If, for example, one outcome is twice as likely to occur as another, we assign the first outcome a probability twice that assigned to the second.
Certain conventions govern the numbers we use as probabilities. It would not make sense for one outcome to be −1 times as likely as another, so we restrict probabilities to be nonnegative numbers. We want probability 0 to correspond to impossibility; an outcome that never occurs would be assigned probability 0. We want probability 1 to correspond to certainty; an outcome that always occurs would be assigned probability 1. In a typical experiment, each outcome's probability is a number bigger than 0 and smaller than 1.
One of the difficulties with traditional, symmetric-key cryptosystems is in getting the two parties to both know a secret key without anybody else knowing. This is particularly difficult when the two parties have never met in person and can only communicate via an insecure channel such as the Internet.
One could try to forestall the above difficulty by providing keys to everybody in advance. However, this introduces another difficulty. Suppose that there are a million and one people that want to participate. We can't know in advance who's going to want to communicate private with whom, so we have to provide each person a million keys, one for each of the other people with whom she might want to communicate. I couldn't possibly remember all these keys, so I have to store them on my computer. Suppose Eve has been eavesdropping on my communication and storing all the messages. If she manages to break into my computer and learn my keys, she can decrypt all these messages.
Worse yet, suppose another person comes along and wants to join the crowd. In order that the new person be able to communicate with everyone else, we have to provide a new key to each of the people already in the crowd. How can we transmit these new keys securely to all these people?
We have seen that the addition cypher is not secure (unless used as a onetime pad). If you stick to one key and use the addition cypher as a block cypher, this cryptosystem is subject to a plaintext–cyphertext attack. Other attacks are effective as well.
There are lots of relatively secure cryptosystems, however. The most famous is DES (Data Encryption Standard). DES came out of an effort in 1970 by the National Bureau of Standards (NBS) to select a standard cryptosystem for use with non-classified data. In 1974, in response to the NBS's public appeal for a cryptosystem, IBM submitted a cryptosystem called Lucifer that they had developed earlier that decade. NBS then went to the National Security Agency (NSA) for help in evaluating the cryptosystem. NSA modified the system somewhat, including a reduction of the key size from the 112 bits used by Lucifer to only 56 bits (a compromise, as NSA had tried to reduce it to 48 bits). The resulting system was then certified by NBS for use. Institutions that needed to communicate privately with the government were expected to use DES (unless the material was classified). In 1979 the American Bankers Association recommended use of DES for encryption. Thus DES achieved very broad use.
We say an integer b evenly divides another integer c if c/b is a whole number. Actually, nobody in mathematics ever says that b “evenly divides” c – people just say b “divides” c. Another way to say the same thing is to say that b is a divisor of c. The divisors of c are the numbers that (evenly) divide c. Finally, one can also say that b is divisible by c.
Examples:
• 3 divides 12.
• 3 is a divisor of 9.
• 40 is not a divisor of 20.
• 40 is divisible by 20.
• 4 divides 4.
• 5 is a divisor of −10.
• 12 divides 60.
• The positive divisors of 50 are 1, 2, 5, 10, 25, and 50.
Relative primality
Two numbers r and s are relatively prime if there is no integer bigger than 1 that is both a divisor of r and a divisor of s. We also say in this case that r is relatively prime to s. For example, 18 and 8 are not relatively prime because 2 is a divisor of both of them. On the other hand, 9 and 8 are relatively prime because the only divisors common to both of them are 1 and −1. We never count 1 and −1 as common divisors when determining relative primality.
A service center is a facility with multiple heterogeneous servers providing specialized service to multiple types of customers. An assignment policy specifies which server is enabled to serve which types of customer, and a routing policy specifies which server a customer will be routed to for service. Thus, a server can be enabled to serve many types of customer, and a customer may have many alternate servers who can serve him. This paper aims to provide decision models to determine optimal static assignment and routing policies, explicitly taking into account the stochastic fluctuations of demand along with the autocorrelations and cross-correlations of the different traffic streams. We consider several possible performance measures and formulate the optimization problem as a mixed integer nonlinear programming problem. We also develop an efficient heuristic algorithm to enhance scalability. Finally, we compare the different policies using the heuristic algorithms. We observe numerically that the routing policy tries to combine the negatively correlated traffic streams, and separate the positively correlated traffic streams.
This paper presents a nonlinear distributed control strategy for flexible-link manipulators to solve the tracking control problem in the joint space and cancel vibrations of the links. First, the dynamic of an n-flexible-link manipulator is decomposed into n subsystems. Each subsystem has a pair of one joint and one link. The distributed control strategy is applied to each subsystem starting from the last subsystem. The strategy of control consists in controlling the nth joint and stabilizing the nth link by assuming that the remaining subsystems are stable. Then, going backward to the (n − 1)th subsystem, the same control strategy is applied to each corresponding joint-link subsystem until the first. Sliding mode technique is used to develop the control law of each subsystem and the global stability of the resulting tracking errors is proved using the Lyapunov technique. This algorithm was tested on a two-flexible-link manipulator and gave effective results, a good tracking performance, and capability to eliminate the links' vibrations.
This paper proposes an analytical method of solving the inverse kinematic problem for a humanoid manipulator with five degrees-of-freedom (DOF) under the condition that the target orientation of the manipulator's end-effector is not constrained around an axis fixed with respect to the environment. Since the number of the joints is less than six, the inverse kinematic problem cannot be solved for arbitrarily specified position and orientation of the end-effector. To cope with the problem, a generalized unconstrained orientation is introduced in this paper. In addition, this paper conducts the singularity analysis to identify all singular conditions.
This paper deals with the design of 3-legged distributed-compliance XYZ compliant parallel manipulators (CPMs) with minimised parasitic rotations, based on the kinematically decoupled 3-PPPRR (P: prismatic joint, and R: revolute joint) and 3-PPPR translational parallel mechanisms (TPMs). The designs are firstly proposed using the kinematic substitution approach, with the help of the stiffness center (SC) overlapping based approach. This is done by an appropriate embedded arrangement so that all of the SCs associated with the passive compliant modules overlap at the point where all of the input forces applied at the input stages intersect. Kinematostatic modelling and characteristic analysis are then carried out for the proposed large-range 3-PPPRR XYZ CPM with overlapping SCs. The results from finite element analysis (FEA) are compared to the characteristics found for the developed analytical models, as are experimental testing results (primary motion) from the prototyped 3-PPPRR XYZ CPM with overlapping SCs. Finally, issues on large-range motion and dynamics of such designs are discussed, as are possible improvements of the actuated compliant P joint. It is shown that the potential merits of the designs presented here include a) minimised parasitic rotations by only using three identical compliant legs; b) compact configurations and small size due to the use of embedded designs; c) approximately kinematostatically decoupled designs capable of easy controls; and d) monolithic fabrication for each leg using existing planar manufacturing technologies such as electric discharge machining (EDM).
We show that the expected time for a random walk on a (multi-)graph G to traverse all m edges of G, and return to its starting point, is at most 2m2; if each edge must be traversed in both directions, the bound is 3m2. Both bounds are tight and may be applied to graphs with arbitrary edge lengths. This has interesting implications for Brownian motion on certain metric spaces, including some fractals.
In this paper we characterise the categories of Lawvere theories and equational theories that correspond to the categories of analytic and polynomial monads on Set, and hence also to the categories of the symmetric and rigid operads in Set. We show that the category of analytic monads is equivalent to the category of regular-linear theories. The category of polynomial monads is equivalent to the category of rigid theories, that is, regular-linear theories satisfying an additional global condition. This solves a problem posed by A. Carboni and P. T. Johnstone. The Lawvere theories corresponding to these monads are identified via some factorisation systems. We also show that the categories of analytic monads and finitary endofunctors on Set are monadic over the category of analytic functors. The corresponding monad for analytic monads distributes over the monad for finitary endofunctors and hence the category of (finitary) monads on Set is monadic over the category of analytic functors. This extends a result of M. Barr.