Thursday, April 28, 2016

The worst presidential race in history


Mike Lupica

So many of the men and women running for President say they are running against Washington and all the meanness there, and divisiveness, and lack of civility. But they have somehow managed to make this campaign look even worse, without shame, as they continue to shame this country in front of the world in the process.
Donald Trump is the angry face of it all, able to out-talk everybody on radio and television, and out-tweet them, and shock the world with his theories about Muslims. But Trump isn’t alone. He’s just the one with the biggest bullhorn, able to out-shout even the bullhorn media.
There has never been a lower point than this in modern presidential politics. If you think there has been, name it. We see how desperate they are to win at all cost. But at what cost to this country? We talk constantly about how unsafe everything has become in a terrorist world. You know what is less safe these days, and more vulnerable than ever? This country’s good name.
“We all talk about how anxious our country has become,” Rep. Pete King was saying yesterday afternoon. “But you know why everybody is anxious? Because they don’t see any leadership from either party, at a time when we’re crying out for that because we’re getting no leadership from the President.”
“You tell me which one of them is going to inspire us?” Pete King said. “Who’s going to lead in a country where we get the idea that we’re now following the lead of the president of France?”

Pete King, out of New York’s 2nd Congressional District, the son of a New York City cop, can turn politics into a bar fight himself sometimes, and you can look that up. But so often these days he seems to have more common sense than almost all of these people who want to be President, especially when he says this:
“Sometimes you worry that this is a campaign about the lowest common denominator.”
The rest of the world does not just see what happens in San Bernardino, in an America where we have a tragedy like San Bernardino every few weeks and sometimes every few days. The rest of the world also sees the response to San Bernardino and the loss of life there now that they can make it all about radical Islam and not an arsenal in the home of Syed Farook and Tashfeen Malik big enough to invade Chicago. The outrage is always more righteous when it is about jihadists and not some redneck shooting up a church in Charleston.
The guns used to murder 14 people were purchased legally, a couple of the fast-killing weapons even purchased for Farook and Malik by some friend of theirs. So once again we heard that no laws were broken, until the body count because of guns grew a little more in America.
But there are never any solutions, just more shouting and name-calling andblaming of Barack Obama, as if the real battleground states are Twitter and cable television. Now members of an entire religion become suspects, or perhaps unindicted co-conspirators. And Texas Sen. Ted Cruz has the same solution that tough guys whose only active service was on the debate team always seem to have: Go over to Syria and blow all the bad guys to kingdom come.
Presidential candidate Donald Trump reacts while addressing supporters at a campaign rally.
“We will utterly destroy ISIS. We will carpet bomb them into oblivion. I don’t know if sand can glow in the dark, but we’re going to find out,” Cruz says, apparently thinking that the mere threat of that will make Abu Bakr al-Baghdadi, the head of the Islamic state, just come out with his hands up.
In that moment, Cruz sounds like Gen. Curtis LeMay 50 years ago, talking about how we needed to do the same thing with North Vietnam.
“We’re going to bomb them back to the Stone Age,” LeMay said, a few years before he was the running mate in 1968 for Alabama Gov. George Wallace, back when it was the two of them who only seemed to be talking to the League of Angry White Guys.
Pete King is right. This has become a battle for the lowest common denominator, and not just with the Republicans. Democrat Hillary Clinton has a chance to stay above it all, but cannot resist making it seem as if all theRepublicans share the same feelings about Muslims and immigrants, as if they are all working off the same playbook when she knows they are not.
“You tell me which one of them is going to inspire us?” Pete King said. “Who’s going to lead in a country where we get the idea that we’re now following the lead of the president of France?”
== RESTRICTED TO EDITORIAL USE / MANDATORY CREDIT: "AFP PHOTO/ HO" / NO SALES / NO MARKETING / NO ADVERTISING CAMPAIGNS / DISTRIBUTED AS A SERVICE TO CLIENTS ==
This is the mud-wrestling Making of the President, 2016, against the backdrop of a world that becomes more dangerous by the day. This is all supposed to be about who would be best for America on the front line against terror. But it’s these candidates who are scaring us half to death.
TAGS:
 
2016 election ,
 
donald trump ,
 
gun control ,
 
pete king ,
 
isis ,
 
ted cruz ,
hillary clinton ,
 
syed rizwan farook ,
 
tashfeen malik ,
 
san bernardino shooting ,
barack obama ,
 
syria



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Thank you IBM via Grubb & Ellis

The work of stationary engineers is varied and complex. We are responsible for the operation, maintenance, renovation and repair of boiler systems and all other mechanical systems in a facility. Stationary engineers are employed in schools, hospitals, hotels, apartment buildings, shopping malls, airports, power plants, industrial and manufacturing plants, breweries, co-generation plants, petro-chemical plants, office and commercial buildings, government facilities and other workplaces. In operating and repairing these facilities, stationary engineers perform work on boilers and steam systems; heating, ventilating and air conditioning systems; building automation systems; diesel engines, turbines, generators; pumps, piping and compressed gas systems; refrigeration and electrical systems and numerous other physical plant functions. We are called stationary engineers because the equipment we operate is similar to equipment operated by locomotive or marine engineers except it is not in a vehicle that moves.
Stationary engineers start up, regulate, repair and shut down equipment. We ensure that equipment operates safely and economically and within established limits by monitoring attached meters, gauges, and computerized controls. We manually control equipment and make the necessary adjustments. We use hand and power tools to perform repairs and maintenance ranging from a complete overhaul to replacing defective valves, gaskets, or bearings. We also record relevant events and facts concerning operation and maintenance in an equipment log. On steam boilers, for example, we observe, control, and record steam pressure, temperature, water level, power output, and fuel consumption. Stationary engineers can often detect potential mechanical problems by observing and listening to the pitch of the machinery. We routinely check safety devices, identifying and correcting any trouble that develops.
Stationary engineers also perform routine maintenance, such as repairing and replacing pumps, motors and other operating equipment, lubricating moving parts, replacing filters, and removing soot and corrosion that can reduce operating efficiency. We also test and chemically treat hydronic systems to prevent corrosion and harmful deposits.
A stationary engineer may be in charge of operation, maintenance and repair of all mechanical systems in a building, industrial power plant or engine room. A chief engineer may direct the work of assistant stationary engineers, turbine operators, boiler tenders, and air-conditioning and refrigeration operators and mechanics. In a small building or industrial plant, there may be only one stationary engineer at a time who will be responsible for the entire operation and maintenance of the building or facility.
What do I need to become a stationary engineer?
  • An average of four years of apprentice training, including on-the-job and classroom training.
  • A good work ethic and responsible attitude.
  • An interest in learning highly technical subjects like boiler operation and maintenance, air conditioning and refrigeration, safety, practical chemistry, elemental physics, instrumentation and controls, electronics and computer controls.
  • A willingness to keep learning. Due to the increasing complexity of the equipment, stationary engineers must continue to update their skills, and many go on to take college courses.
  • The ability to do shift work, and to work on weekends and holidays.
How do I get accepted into an IUOE apprentice training program for stationary engineers?
Selection criteria vary from one local to another, so you should contact an IUOE stationary local in your area for specific information. However, minimum requirements include that applicants be at least 18 years old, have a high school diploma or GED, be legal to work and drug free. Having previous mechanical or technical experience would be helpful, but may not be necessary.
What training does IUOE provide for apprentice stationary engineers?
High quality, skill development training is provided by apprenticeship and training programs at IUOE stationary local unions. These recognized programs are jointly sponsored by IUOE local unions and the employers who hire stationary engineers.
The average length of a stationary engineer apprenticeship is four years. During this period, apprentices learn their craft by working with skilled stationary engineers at an actual workplace, and by attending related classroom instruction. In some cases, apprentice training is supplemented by courses at trade or technical schools and, due to the increasing complexity of the equipment with which they work, many stationary engineers have also taken college courses. Training is critical to preparing apprentices to be tested for stationary engineer licenses, which is required by most states.
Journey level stationary engineers are often encouraged by their employers to continue their education. Many IUOE locals offer free training to their members to help them broaden their skills, keep up with changes in the industry and improve their employability. Additional training has helped many IUOE members move up to management or supervisory positions.
How much do stationary engineers earn?
Journey level and apprentice wages vary considerably from one part of the country to another, so you will need to contact an IUOE local in your area for specific information. Starting pay for an apprentice varies from 45% to 60% of the journey level rate. Pay increases are scheduled at designated times during apprenticeship and are negotiated as part of each local’s contract with employers. During the final year of apprenticeship, wages are typically 80% to 95% of the journey level rate.
- See more at: http://www.iuoe.org/jobs/stationary-engineer#sthash.gvwc8DpF.dpuf
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Fuzzy adaptive control system of a non-stationary plant with closed-loop passive identifier


Abstract

Typically chemical processes have significant nonlinear dynamics, but despite this, industry is conventionally still using PID-based regulatory control systems. Moreover, process units are interconnected, in terms of inlet and outlet material/energy flows, to other neighboring units, thus their dynamic behavior is strongly influenced by these connections and, as a consequence, conventional control systems performance often proves to be poor.
This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller, also exploiting the results coming from an identification procedure that is carried on when an unmeasured step disturbance of any shape affects the process behavior.
In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.

Keywords

  • PID-controller; 
  • Identification; 
  • Fuzzy controller; 
  • Closed-loop; 
  • Unknown disturbances;
  • Auto-tuning control

1. Introduction

Nowadays the conventional proportional-integral-derivative (PID) controllers are the most widely used for process control in most of the industrial plants. The success of PID control logic can be attributed to the achievement of simple structures of automatic control systems (ACS) and its effectiveness for linear systems [1], [2], [3], [4], [5],[6] and [7]. There is a wide variety of PID controllers tuning rules: the Ziegler-Nichols rule[8], [9] and [10], the magnitude optimum method [11], [12], [13], [14], [15] and [16], the direct synthesis methods [17] and [18], the Internal Model Control methods [9], [19],[20] and [21], the minimum error integral criteria [22], [23] and [24], the iterative feedback tuning method [25], the virtual reference feedback tuning method [26] and [27], the approximate M-constrained integral gain optimization method [28], AMIGO method [29]and others. The required quality of a PID control system can be achieved by means of a variety of tuning rules once a linear model of the controlled system and a criteria for the assessment of the control performance are chosen.
Usually the conventional PID controller is not effective for complex dynamic systems[30] and [31]. The complex dynamic systems are those systems with non-linear static characteristics, i.e. those systems that are described by differential equations with time-varying parameters. This feature essentially complicates the design and analysis of PID-based control systems and decreases their control performance.
A number of researchers have conducted studies to combine a conventional PID controller with a fuzzy logic controller (FLC) in order to achieve a better control quality in ACS rather than the one guaranteed by conventional PID controllers. The idea of using fuzzy sets [32] is successfully applied, for the first time, in the control of a dynamic plant developed by Mamdani and Assilian [33]. Currently, there are different types of FLC, but a PID-based FLC is the most common and practical for applications to ACS [34], [35],[36], [37] and [38]. Such FLC is equivalent to a conventional PID controller for the input-output structure [34] and [39]. PID-based FLC may be constructed by sequentially incorporating FLC and PID controllers or paralleling PID and FLC (PID with an adapter based on FLC). Moreover, the use of FLC logic makes it easy to add nonlinearities and additional input signals to the control law [1], that, in turn, allows to apply PID-based FLC to complex dynamic systems.
A priori information about the dynamics of the controlled plant is required for the synthesis of PID-based FLC. Hammerstein and Wiener models may be used to describe complex dynamics real-life processes [40], [41], [42] and [43]. Hammerstein and Wiener models are methodologies constituted by the combination of a static nonlinearity (N) and a linear system (L), respectively in the N-L and L-N form. The problem of identifying N and L from input-output data has attracted and attracts a lot of research interests and many methods are available for this problem in literature [40], [41], [42], [43], [44],[45] and [46]. The nonlinear dynamic system can be approximated by a linear dynamic system near the operating point, which is sufficient for PID tuning. It is not a simple task to define the parameters of the linear dynamic model approximation in the closed-loop system. In [47], [48] and [49] active methods of identification are proposed; here sine waves in input are used to excite the Wiener continuous-time system and frequency methods are used to determine the unknowns. Unknown additive disturbances create problems for closed-loop identification [50]. Good results can be obtained by using MATLAB system identification toolbox for the identification of the parameters of the process with the use of ARX, ARMAX, BJ state space, polynomial models and others[51].
Practically, in chemical and nuclear industries (i.e. integrated separations, extractions[52] and [53], crystallization processes to purify U and Pu from other fusion side-components) any processing step has a high level of automation but, in the contrary an insufficient automation in process control occurs. Moreover field operators need to work within the control loops of complex physicochemical processes. On the one hand, all processes are high responsibility technology (HRT), i.e. high performance technology with respect of safety level. On the other hand, they are also complex dynamic systems.
The purpose of the research is to develop a method of synthesis for low-level ACS (relative to HRT), which will provide the required control performance also in the presence of a significant change in the process parameters and several step disturbances with unknown amplitudes and durations. A Low-level ACS must fulfill the following limitations: control in the tight real-time mode should be performed with hot standby of the controllers; applied controllers have limited computation abilities which do not allow an extension of the mathematical support functions; for the purpose of control, conventional PID controllers should be employed.

2. Material and methods for the fuzzy adaptive control of a generic plant

The proposed method employs algorisms for the plant identification coupled with fuzzy systems such as Mamdami controllers [54] and [55]. The layout of a generic ACS plant is presented in Fig. 1 while a scheme of an adaptive fuzzy controller is shown in Fig. 2.
Fuzzy adaptive control system. g : reference signal; f* : non-measurable ...
Fig. 1. 
Fuzzy adaptive control system. g : reference signal; f* : non-measurable disturbance; f : measurable disturbance; Pu : plant control channel; Pf : plant disturbance channel; Pf* : plant non-measurable disturbance channel; y: controlled variable; ε : control error is defined as ε = g – y.
Figure options
Adaptive fuzzy controller for an ACS.
Fig. 2. 
Adaptive fuzzy controller for an ACS.
Figure options
The optimization problem consists of maximizing or minimizing a functional which plays the key role from the viewpoint of the design of adaptive and optimal control systems. It is addressed here in the following form:
equation1
min(Jek+Juk+Jnk)
Turn MathJaxon
where
equation2
View the MathML source
equation3
View the MathML source
Jnk– the number of control error oscillations in the interval he, (2)
where k = 1,2, … ∞, εj – the control error, uj – the manipulated variable, he – the control error interval, hu – the control interval, j – the index of time sampling.
The adaptor-optimizer of the suggested ACS (see Fig. 2) includes the following blocks: an identifier, a fuzzy rules base generator, a Mamdani fuzzy output controller and Jn, Jeand Ju terms calculation engines. The identification is performed in the closed-loop system in those operating conditions where the edge of the transient is reached (seeFig. 4).
Parameters of control object, obtained as a result the identification transmitted into generator fuzzy rules and used to calculate parameters controller by the magnitude optimum method. Obtained controller parameters are used to optimize the algorithm, which is shown in Fig. 3.
The principle of operation of the generator fuzzy rules.
Fig. 3. 
The principle of operation of the generator fuzzy rules.
Figure options
The variation in the controlled variable, caused by the change of the non-measurable disturbance f*, is considered the initial signal for the identification procedure. The time instant t0 where the non-measurable step disturbance f* undergoes a step change is unknown. The time instant t1 is defined by the deviation threshold of y from g by Δy  >  yg, where yg is the required control accuracy, and t2 is the time instant of the y variable sign change. The parameters of the plant are defined by Levenberg-Marquardt optimization method. In this case, the measured disturbance f, the control action u and the controlled variable y (see Fig. 1, Fig. 2, Fig. 3 and Fig. 4) are supplied to the identifier input in the time interval whose lower and upper bounds are, respectively, t2 and t3.
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