Mechatronics (Part-XIV)- Exploring Industrial Automation

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Check Out The Significant Roles Of Automation In Industries

Here we continue with the fourteenth part of our blog on mechatronics. Those who have missed our thirteenth blog can read it from Here. It will help to connect with the fourteenth part of the blog discussing about the significant applications of automation in industries. Let us explore the blog to find out in more details. Automation and technology would be a great boon if it were creative, if were more leisure, more opportunity to engage in raising family, providing guidance to the young, all the staff we say we need.

What is Industrial Automation?

Industrial automation deals primarily with the automation of manufacturing, quality control and material handling processes. General purpose controllers for industrial processes include Programmable logic controllers, stand-alone I/O modules, and computers. Industrial automation is to replace the decision making of humans and manual command-response activities with the use of mechanised equipment and logical programming commands. One trend is increased use of Machine vision to provide automatic inspection and robot guidance functions; another is a continuing increase in the use of robots. Industrial automation is simply required in industries. The integration of control and information across the enterprise enables industries to optimise industrial process operations. Energy efficiency in industrial processes has become a higher priority. Semiconductor companies like Infineon Technologies are offering 8-bit micro-controller applications for example found in motor controls, general purpose pumps, fans, and e-bikes to reduce energy consumption and thus increase efficiency.

Industry 4.0

The rise of industrial automation is directly tied to the “fourth industrial revolution”, which is better known now as Industry 4.0. Originating from Germany, Industry 4.0 encompasses numerous devises, concepts, and machines. It, along with the advancement of the Industrial Internet of Things (formally known as the IoT or IIoT) which is “Internet of Things is a seamless integration of diverse physical objects in the Internet through a virtual representation”. These new revolutionary advancements have drawn attention to the world of automation in an entirely new light and shown ways for it to grow to increase productivity and efficiency in machinery and manufacturing facilities. Industry 4.0 works with the IIoT and software/hardware to connect in a way that (through communication technologies) add enhancements and improve manufacturing processes. Being able to create smarter, safer, and more advanced manufacturing is now possible with these new technologies. It opens up a manufacturing platform that is more reliable, consistent, and efficient that before. Implementation of systems such as SCADA is an example of software that takes place in Industrial Automation today. SCADA is supervisory data collection software, just one of the many used in Industrial Automation. Industry 4.0 vastly covers many areas in manufacturing and will continue to do so as time goes on.

Industrial Robotics

Industrial robotics is a sub-branch in the industrial automation that aids in various manufacturing processes. Such manufacturing processes include; machining, welding, painting, assembling and material handling to name a few. Industrial robots utilizes various mechanical, electrical as well as software systems to allow for high precision, accuracy and speed that far exceeds any human performance. The birth of industrial robot came shortly after World War II as United States saw the need for a quicker way to produce industrial and consumer goods. Servos, digital logic and solid state electronics allowed engineers to build better and faster systems and overtime these systems were improved and revised to the point where a single robot is capable of running 24 hours a day with little or no maintenance. In 1997, there were 700,000 industrial robots in use, the number has risen to 1.8M in 2017.

To be continued in the next blog...

Mechatronics (Part-XIII)- Smart Automations In Highways, Home And Laboratory Technologies

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Know About The Applications Of Smart Automations In Highways, Home and Laboratory Technologies

Now we continue with the thirteenth part of our blog on mechatronics. Those who have missed our twelfth blog can read it from Here. It will help to connect with the thirteenth part of the blog discussing about the applications of smart automations in highways, home and laboratory technologies. Let us explore the blog to find out in more details. By 2018, automation is going to be in full swing in the world. There are estimates that it could replace 50 percent of our jobs. That is an enormous shift. But even if we go through a phase where we have an unemployment valley from automation, there will be new jobs and new things for us to do.

How Automation Emerged in Highway?

As demands for safety and mobility have grown and technological possibilities have multiplied, interest in automation has grown. Seeking to accelerate the development and introduction of fully automated vehicles and highways, the United States Congress authorized more than $650 million over six years for intelligent transport systems (ITS) and demonstration projects in the 1991 Intermodal Surface Transportation Efficiency Act (ISTEA). Congress legislated in ISTEA that "the Secretary of Transportation shall develop an automated highway and vehicle prototype from which future fully automated intelligent vehicle-highway systems can be developed. Such development shall include research in human factors to ensure the success of the man-machine relationship. The goal of this program is to have the first fully automated highway roadway or an automated test track in operation by 1997. This system shall accommodate installation of equipment in new and existing motor vehicles". Full automation commonly defined as requiring no control or very limited control by the driver; such automation would be accomplished through a combination of sensor, computer, and communications systems in vehicles and along the roadway. Fully automated driving would, in theory, allow closer vehicle spacing and higher speeds, which could enhance traffic capacity in places where additional road building is physically impossible, politically unacceptable, or prohibitively expensive. Automated controls also might enhance road safety by reducing the opportunity for driver error, which causes a large share of motor vehicle crashes. Other potential benefits include improved air quality (as a result of more-efficient traffic flows), increased fuel economy, and spin-off technologies generated during research and development related to automated highway systems.

Automation in Waste Management and Home

Automated waste collection trucks prevent the need for as many workers as well as easing the level of labour required to provide the service. Home automation (also called domotics) designates an emerging practice of increased automation of household appliances and features in residential dwellings, particularly through electronic means that allow for things impracticable, overly expensive or simply not possible in recent past decades.

Automation in Laboratory Technology

Automation is essential for many scientific and clinical applications. Therefore, automation has been extensively employed in laboratories. From as early as 1980 fully automated laboratories have already been working. However, automation has not become widespread in laboratories due to its high cost. This may change with the ability of integrating low-cost devices with standard laboratory equipment. Auto samplers are common devices used in laboratory automation.

To be continued in the next blog...

Mechatronics (Part-XII)- Automation In Recent And Emerging Applications

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Check Out The Role Of Automation In Recent And Emerging Applications

Here we continue with the twelfth part of our blog on mechatronics. Those who have missed our eleventh blog can read it from Here. It will help to connect with the twelfth part of the blog discussing about the different role of automation in recent and emerging applications. Let us explore the blog to find out in more details. Automation provides us with wondrous increases of production and information, but does it tell us what to do with men the machines displace? Modern industry gives us the capacity for unparallel wealth-but where is our capacity to make the wealth meaningful to the poor of every nation?

Cognitive Automation

Cognitive automation, as a subset of artificial intelligence is an emerging genus of automation enabled by cognitive computing. Its primary concern is the automation of clerical tasks and workflows that consist of structuring unstructured data. Cognitive automation relies on multiple disciplines: natural language processing, real-time computing, machine learning algorithms, big data analytics and evidence-based learning. According to Deloitte, cognitive automation enables the replication of human tasks and judgment "at rapid speeds and considerable scale". Such tasks include:

• Document redaction
• Data extraction and document synthesis / reporting
• Contract management
• Natural language search
• Customer, employee, and stakeholder on boarding
• Manual activities and verification
• Follow up and email communications

Recent and Emerging Applications- Automated Retail

i) Food and Drink

The food retail industry has started to apply automation to the ordering process; McDonald's has introduced touch screen ordering and payment systems in many of its restaurants, reducing the need for as many cashier employees. The University of Texas at Austin has introduced fully automated cafe retail locations. Some Cafes and restaurants have utilized mobile and tablet "apps" to make the ordering process more efficient by customers ordering and paying on their device. Some restaurants have automated food delivery to customers’ tables using a Conveyor belt system. The use of robots is sometimes employed to replace waiting staff.

ii) Stores

Many supermarkets and even smaller stores are rapidly introducing Self checkout systems reducing the need for employing checkout workers. In the United States, the retail industry employs 15.9 million people as of 2017 (around 1 in 9 Americans in the workforce). Globally, an estimated 192 million workers could be affected by automation according to research by Eurasia Group. 

Online shopping could be considered a form of automated retail as the payment and checkout are through an automated Online transaction processing system, with the share of online retail accounting jumping from 5.1% in 2011 to 8.3% in 2016. However, two-thirds of books, music and films are now purchased online. In addition, automation and online shopping could reduce demands for shopping malls, and retail property, which in America is currently estimated to account for 31% of all commercial property or around 7 billion square feet. Amazon has gained much of the growth in recent years for online shopping, accounting for half of the growth in online retail in 2016. Other forms of automation can also be an integral part of online shopping, for example the deployment of automated warehouse robotics such as that applied by Amazon using Kiva Systems.

iii) Automated Mining

Automated mining involves the removal of human labor from the mining process. The mining industry is currently in the transition towards automation. Currently it can still require a large amount of human capital, particularly in the third world where labor costs are low so there is less incentive for increasing efficiency through automation.

iv) Automated Video Surveillance

The Defense Advanced Research Projects Agency (DARPA) started the research and development of automated visual surveillance and monitoring (VSAM) program, between 1997 and 1999, and airborne video surveillance (AVS) programs, from 1998 to 2002. Currently, there is a major effort underway in the vision community to develop a fully automated tracking surveillance system. Automated video surveillance monitors people and vehicles in real time within a busy environment. Existing automated surveillance systems are based on the environment they are primarily designed to observe, i.e., indoor, outdoor or airborne, the amount of sensors that the automated system can handle and the mobility of sensor, i.e., stationary camera vs. mobile camera. The purpose of a surveillance system is to record properties and trajectories of objects in a given area, generate warnings or notify designated authority in case of occurrence of particular events.

To be continued in the next blog...

 

Mechatronics (Part-XI)- Automation Tools And Limitations

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Know About The Different Automation Tools And Their Limitations

Now we continue with the eleventh part of our blog on mechatronics. Those who have missed our tenth blog can read it from Here. It will help to connect with the eleventh part of the blog discussing different automation tools and their limitations. Let us explore the blog to find out in more details. Automation is a great profit but it's a real potential trouble area for society. Automation is going to cause unemployment, and we need to prepare it.

Automation Tools

Engineers can now have numerical control over automated devices. The result has been a rapidly expanding range of applications and human activities. Computer-aided technologies now serve as the basis for mathematical and organizational tools used to create complex systems. Notable examples of CAx include Computer-aided design(CAD software) and Computer-aided manufacturing (CAM software). The improved design, analysis, and manufacture of products enabled by CAx has been beneficial for industry. Information technology, together with industrial machinery and processes, can assist in the design, implementation, and monitoring of control systems. One example of an industrial control system is a programmable logic controller (PLC). PLCs are specialized hardened computers which are frequently used to synchronize the flow of inputs from (physical) sensors and events with the flow of outputs to actuators and events. Human-machine interfaces (HMI) or computer human interfaces (CHI), formerly known as man-machine interfaces, are usually employed to communicate with PLCs and other computers. Service personnel who monitor and control through HMIs can be called by different names. In industrial process and manufacturing environments, they are called operators or something similar. In boiler houses and central utilities departments they are called stationary engineers. Different types of automation tools exist:

• ANN –Artificial neural network
• DCS –Distributed Control System
• HMI –Human Machine Interface
• SCADA –Supervisory Control and Data Acquisition
• PLC –Programmable Logic Controller
• Instrumentation
• Motion control
• Robotics

When it comes to factory automation, Host Simulation Software (HSS) is a commonly used testing tool that is used to test the equipment software. HSS is used to test equipment performance with respect to Factory Automation standards (timeouts, response time, processing time).

Limitations to Automation

Current technology is unable to automate all the desired tasks. Many operations using automation have large amounts of invested capital and produce high volumes of product, making malfunctions extremely costly and potentially hazardous. Therefore, some personnel are needed to ensure that the entire system functions properly and that safety and product quality are maintained. As a process becomes increasingly automated, there is less and less labor to be saved or quality improvement to be gained. This is an example of both diminishing returns and the logistic function. As more and more processes become automated, there are fewer remaining non-automated processes. This is an example of exhaustion of opportunities. New technological paradigms may however set new limits that surpass the previous limits.

Current Limitation

Many roles for humans in industrial processes presently lie beyond the scope of automation. Human-level pattern recognition, language comprehension, and language production ability are well beyond the capabilities of modern mechanical and computer systems (but see Watson (computer)). Tasks requiring subjective assessment or synthesis of complex sensory data, such as scents and sounds, as well as high-level tasks such as strategic planning, currently require human expertise. In many cases, the use of humans is more cost-effective than mechanical approaches even where automation of industrial tasks is possible. Overcoming these obstacles is a theorized path to post-scarcity economics.

Paradox Automation

The paradox of automation says that the more efficient the automated system, the more crucial the human contribution of the operators. Humans are less involved, but their involvement becomes more critical.If an automated system has an error, it will multiply that error until it’s fixed or shut down. This is where human operators come in.A fatal example of this was Air France Flight 447, where a failure of automation put the pilots into a manual situation they were not prepared for. 

To be continued in the next blog...

Mechatronics (Part-X)- Automation In Manufacturing And Health Industries

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Know About The Role Of Automation In Various Sectors Like Manufacturing And Health Industries

Here we continue with the tenth part of our blog on mechatronics. Those who have missed our ninth blog can read it from Here. It will help to connect with the tenth part of the blog discussing about the role of automation in manufacturing and health industries. Let us explore the blog to find out in more details. In words of Alain Dehaze:

"Warp speed developments in technology-automation,artificial intelligence, and the arrival of the sharing economy-are transforming how we work.Beyond technology, traditional working patterns are also being disrupted by changes in society,organizations and workforce management,leading to the rise of a more independent and dispersed workforce".

Automation in Manufacturing

Lights out manufacturing is a production system with no human workers, to eliminate labor costs. Lights Out Manufacturing grew in popularity in the U.S. when General Motors in 1982 implemented humans “hands-off” manufacturing in order to “replace risk-averse bureaucracy with automation and robots”. However, the factory never reached full “lights out” status. The expansion of Lights Out Manufacturing requires:

• Reliability of equipment
• Long term mechanic capabilities
• Planned preventative maintenance
• Commitment from the staff

Automation in Health

The costs of automation to the environment are different depending on the technology, product or engine automated. There are automated engines that consume more energy resources from the Earth in comparison with previous engines and vice versa.[citation needed] Hazardous operations, such as oil refining, the manufacturing of industrial chemicals, and all forms of metal working, were always early contenders for automation. The automation of vehicles could prove to have a substantial impact on the environment, although the nature of this impact could be beneficial or harmful depending on several factors. Because automated vehicles are much less likely to get into accidents compared to human-driven vehicles, some precautions built into current models (such as anti-lock brakes or laminated glass) would not be required for self-driving versions. Removing these safety features would also significantly reduce the weight of the vehicle, thus increasing fuel economy and reducing emissions per mile. Self-driving vehicles are also more precise with regard to acceleration and breaking, and this could contribute to reduced emissions. Self-driving cars could also potentially utilize fuel-efficient features such as route mapping that is able to calculate and take the most efficient routes. Despite this potential to reduce emissions, some researchers theorize that an increase of production of self-driving cars could lead to a boom of vehicle ownership and use. This boom could potentially negate any environmental benefits of self-driving cars if a large enough number of people begin driving personal vehicles more frequently. Automation of homes and home appliances is also thought to impact the environment, but the benefits of these features are also questioned. A study of energy consumption of automated homes in Finland showed that smart homes could reduce energy consumption by monitoring levels of consumption in different areas of the home and adjusting consumption to reduce energy leaks (such as automatically reducing consumption during the nighttime when activity is low). This study, along with others, indicated that the smart home’s ability to monitor and adjust consumption levels would reduce unnecessary energy usage. However, new research suggests that smart homes might not be as efficient as non-automated homes. A more recent study has indicated that, while monitoring and adjusting consumption levels does decrease unnecessary energy use, this process requires monitoring systems that also consume a significant amount of energy. This study suggested that the energy required to run these systems is so much so that it negates any benefits of the systems themselves, resulting in little to no ecological benefit.

Convertibility and Turnaround Time

Another major shift in automation is the increased demand for flexibility and convertibility in manufacturing processes. Manufacturers are increasingly demanding the ability to easily switch from manufacturing Product A to manufacturing Product B without having to completely rebuild the production lines. Flexibility and distributed processes have led to the introduction of Automated Guided Vehicles with Natural Features Navigation. Digital electronics helped too. Former analogue-based instrumentation was replaced by digital equivalents which can be more accurate and flexible, and offer greater scope for more sophisticated configuration, parametrization and operation. This was accompanied by the fieldbus revolution which provided a networked (i.e. a single cable) means of communicating between control systems and field level instrumentation, eliminating hard-wiring. Discrete manufacturing plants adopted these technologies fast. The more conservative process industries with their longer plant life cycles have been slower to adopt and analogue-based measurement and control still dominates. The growing use of Industrial Ethernet on the factory floor is pushing these trends still further, enabling manufacturing plants to be integrated more tightly within the enterprise, via the internet if necessary. Global competition has also increased demand for Reconfigurable Manufacturing Systems.

Continued in the next blog...

Mechatronics (Part-IX)- Significant Applications Of Automations In Space and Computers

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Find Out The Important Applications Of Automations In Computers and Space

Now we continue with the ninth part of our blog on mechatronics. Those who have missed our eighth blog can read it from Here. It will help to connect with the ninth part of the blog discussing about the advantages, disadvantages and societal impact of automationimportant applications of automations in space and computers. Let us explore the blog to find out in more details. In words of Mark Cuban:

“Whatever you are studying right now, if you are not getting up to speed on deep learning, neutral networks, and so on you lose. We are going through the process where software will automate software, automation will automate automation.”

Significant Applications of Automations

The automatic telephone switchboard was introduced in 1892 along with dial telephones. By 1929, 31.9% of the Bell system was automatic. Automatic telephone switching originally used vacuum tube amplifiers and electro-mechanical switches, which consumed a large amount of electricity. Call volume eventually grew so fast that it was feared the telephone system would consume all electricity production, prompting Bell Labs to begin research on the transistor. The logic performed by telephone switching relays was the inspiration for the digital computer. The first commercially successful glass bottle blowing machine was an automatic model introduced in 1905. The machine, operated by a two-man crew working 12-hour shifts, could produce 17,280 bottles in 24 hours, compared to 2,880 bottles made by a crew of six men and boys working in a shop for a day. The cost of making bottles by machine was 10 to 12 cents per gross compared to $1.80 per gross by the manual glassblowers and helpers. Sectional electric drives were developed using control theory. Sectional electric drives are used on different sections of a machine where a precise differential must be maintained between the sections. In steel rolling, the metal elongates as it passes through pairs of rollers, which must run at successively faster speeds. In paper making the paper sheet shrinks as it passes around steam heated drying arranged in groups, which must run at successively slower speeds. The first application of a sectional electric drive was on a paper machine in 1919. One of the most important developments in the steel industry during the 20th century was continuous wide strip rolling, developed by Armco in 1928. Before automation many chemicals were made in batches. Self-acting machine tools that displaced hand dexterity so they could be operated by boys and unskilled laborers were developed by James Nasmyth in the 1840s. Machine tools were automated with Numerical control (NC) using punched paper tape in the 1950s. This soon evolved into computerized numerical control (CNC). Today extensive automation is practiced in practically every type of manufacturing and assembly process. Some of the larger processes include electrical power generation, oil refining, chemicals, steel mills, plastics, cement plants, fertilizer plants, pulp and paper mills, automobile and truck assembly, aircraft production, glass manufacturing, natural gas separation plants, food and beverage processing, canning and bottling and manufacture of various kinds of parts. Robots are especially useful in hazardous applications like automobile spray painting. Robots are also used to assemble electronic circuit boards. Automotive welding is done with robots and automatic welders are used in applications like pipelines.

Space and Computer Advancements

With the advent of the space age in 1957, controls design, particularly in the United States, turned away from the frequency-domain techniques of classical control theory and backed into the differential equation techniques of the late 19th century, which were couched in the time domain. During the 1940s and 1950s, German mathematician Irmgard Flugge-Lotz developed the theory of discontinuous automatic control, which became widely used in hysteresis control systems such as navigation systems, fire-control systems, and electronics. Through Flugge-Lotz and others, the modern era saw time-domain design for nonlinear  systems (1961), navigation (1960), optimal control and estimation theory (1962), nonlinear control theory (1969), digital control and filtering theory (1974), and the personal computer (1983).

Continued in the next blog...

Mechatronics (Part-VIII)- Advantages, Disadvantages And Societal Impact Of Automation

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Find Out The Advantages, Disadvantages and Societal Impact Of Automation

Here we continue with the eighth part of our blog on mechatronics. Those who have missed our seventh blog can read it from Here. It will help to connect with the eighth part of the blog discussing about the advantages, disadvantages and societal impact of automation. Let us explore the blog to find out in more details. In words of Tom Peters:

“For a blue-collar worker, the driving force behind change was factory automation using programmable machine tools. For an office worker, it’s office automation using computer technology: enterprise-resource-planning systems, groupware, intranets, extranets, expert systems, the Web and E-Commerce.”

Advantages and Disadvantages of Automation

Perhaps the most cited advantage of automation in industry is that it is associated with faster production and cheaper labor costs. Another benefit could be that it replaces hard, physical, or monotonous work. Additionally, tasks that take place in hazardous environments or that are otherwise beyond human capabilities can be done by machines, as machines can operate even under extreme temperatures or in atmospheres that are radioactive or toxic. They can also be maintained with simple quality checks. However, at the time being, not all tasks can be automated, and some tasks are more expensive to automate than others. Initial costs of installing the machinery in factory settings are high, and failure to maintain a system could result in the loss of the product itself. Moreover, some studies seem to indicate that industrial automation could impose ill effects beyond operational concerns, including worker displacement due to systemic loss of employment and compounded environmental damage; however, these findings are both convoluted and controversial in nature, and could potentially be circumvented. The main advantages of automation are-

• Increased throughput or productivity.
• Improved quality or increased predictability of quality.
• Improved robustness (consistency), of processes or product.
• Increased consistency of output.
• Reduced direct human labor costs and expenses.
• Installation in operations reduces cycle time.
• Can complete tasks where a high degree of accuracy is required.
• Replaces human operators in tasks that involve hard physical or monotonous work (e.g., using one forklift with a single driver instead of a team of multiple workers to lift a heavy object)
• Reduces some occupational injuries (e.g., fewer strained backs from lifting heavy objects)
• Replaces humans in tasks done in dangerous environments (i.e. fire, space, volcanoes, nuclear facilities, underwater, etc.)
• Performs tasks that are beyond human capabilities of size, weight, speed, endurance, etc.
• Reduces operation time and work handling time significantly.
• Frees up workers to take on other roles.
• Provides higher level jobs in the development, deployment, maintenance and running of the automated processes.

The main disadvantages of automation are:

• Possible security threats/vulnerability due to increased relative susceptibility for committing errors.
• Unpredictable or excessive development costs.
• High initial cost.
• Displaces workers due to job replacement.
• Leads to further environmental damage and could compound climate change

Societal Impact of Automation

Increased automation can often cause workers to feel anxious about losing their jobs as technology renders their skills or experience unnecessary. Early in the Industrial Revolution, when inventions like the steam engine were making some job categories expendable, workers forcefully resisted these changes. Luddites, for instance, were English textile workers who protested the introduction of weaving machines by destroying them. Similar movements have sprung up periodically ever since. For most of the nineteenth and twentieth centuries, the most influential of these movements were led by organized labor, which advocated for the retraining of workers whose jobs were rendered redundant by machines. Currently, the relative anxiety about automation reflected in opinion polls seems to correlate closely with the strength of organized labor in that region or nation. For example, while a recent study by the Pew Research Center indicated that 72% of Americans are worried about increasing automation in the workplace, 80% of Swedes see automation and artificial intelligence as a good thing, due to the country’s still-powerful unions and a more robust national safety net. Automation is already contributing significantly to unemployment, particularly in nations where the government does not proactively seek to diminish its impact. In the United States, 47% of all current jobs have the potential to be fully automated by 2033, according to the research of experts Carl Benedikt Frey and Michael Osborne. Furthermore, wages and educational attainment appear to be strongly negatively correlated with an occupation’s risk of being automated. Prospects are particularly bleak for occupations that do not presently require a university degree, such as truck driving. Even in high-tech corridors like Silicon Valley, concern is spreading about a future in which a sizable percentage of adults have little chance of sustaining gainful employment. As the example of Sweden suggests, however, the transition to a more automated future need not inspire panic, if there is sufficient political will to promote the retraining of workers whose positions are being rendered obsolete.

Continued in the next blog...

Mechatronics (Part-VII)- Control Systems In Automation

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Find Out In Details About The Control Systems Used In Automation

Now we continue with the seventh part of our blog on mechatronics. Those who have missed our sixth blog can read it from Here. It will help to connect with the seventh part of the blog discussing about control systems used in automation. Let us explore the blog to find out in more details.

Open and Closed Loop Control in Automation

Fundamentally, there are two types of control loop; open loop control, and closed loop feedback control. In open loop control, the control action from the controller is independent of the "process output" (or "controlled process variable"). A good example of this is a central heating boiler controlled only by a timer, so that heat is applied for a constant time, regardless of the temperature of the building. (The control action is the switching on/off of the boiler. The process output is the building temperature). In closed loop control, the control action from the controller is dependent on the process output. In the case of the boiler analogy this would include a thermostat to monitor the building temperature, and thereby feedback a signal to ensure the controller maintains the building at the temperature set on the thermostat. A closed loop controller therefore has a feedback loop which ensures the controller exerts a control action to give a process output the same as the "Reference input" or "set point". For this reason, closed loop controllers are also called feedback controllers. The definition of a closed loop control system according to the British Standard Institution is 'a control system possessing monitoring feedback, the deviation signal formed as a result of this feedback being used to control the action of a final control element in such a way as to tend to reduce the deviation to zero.' Likewise, a Feedback Control System is a system which tends to maintain a prescribed relationship of one system variable to another by comparing functions of these variables and using the difference as a means of control. The advanced type of automation that revolutionized manufacturing, aircraft, communications and other industries, is feedback control, which is usually continuous and involves taking measurements using a sensor and making calculated adjustments to keep the measured variable within a set range. The theoretical basis of closed loop automation is control theory.

Control Actions

1. Discrete Control (On/Off)

One of the simplest types of control is on-off control. An example is the thermostat used on household appliances which either opens or closes an electrical contact. (Thermostats were originally developed as true feedback-control mechanisms rather than the on-off common household appliance thermostat.) Sequence control, in which a programmed sequence of discrete operations is performed, often based on system logic that involves system states. An elevator control system is an example of sequence control.

2. PID Controller 

A proportional–integral–derivative controller (PID controller) is a control loop feedback mechanism (controller) widely used in industrial control systems. In a PID loop, the controller continuously calculates an error value  as the difference between a desired set point and a measured process variable and applies a correction based on proportional, integral, and derivative terms, respectively (sometimes denoted P, I, and D) which give their name to the controller type. The theoretical understanding and application dates from the 1920s, and they are implemented in nearly all analogue control systems; originally in mechanical controllers, and then using discrete electronics and latterly in industrial process computers.

3. Sequential Control and Logical Sequence on System State Control

Sequential control may be either to a fixed sequence or to a logical one that will perform different actions depending on various system states. An example of an adjustable but otherwise fixed sequence is a timer on a lawn sprinkler. States refer to the various conditions that can occur in a use or sequence scenario of the system. An example is an elevator, which uses logic based on the system state to perform certain actions in response to its state and operator input. For example, if the operator presses the floor n button, the system will respond depending on whether the elevator is stopped or moving, going up or down, or if the door is open or closed, and other conditions. An early development of sequential control was relay logic, by which electrical relays engage electrical contacts which either start or interrupt power to a device. Relays were first used in telegraph networks before being developed for controlling other devices, such as when starting and stopping industrial-sized electric motors or opening and closing solenoid valves. Using relays for control purposes allowed event-driven control, where actions could be triggered out of sequence, in response to external events. These were more flexible in their response than the rigid single-sequence cam timers. More complicated examples involved maintaining safe sequences for devices such as swing bridge controls, where a lock bolt needed to be disengaged before the bridge could be moved, and the lock bolt could not be released until the safety gates had already been closed. The total number of relays, cam timers and drum sequencers can number into the hundreds or even thousands in some factories. Early programming techniques and languages were needed to make such systems manageable, one of the first being ladder logic, where diagrams of the interconnected relays resembled the rungs of a ladder. Special computers called programmable logic controllers were later designed to replace these collections of hardware with a single, more easily re-programmed unit. In a typical hard wired motor start and stop circuit (called a control circuit) a motor is started by pushing a "Start" or "Run" button that activates a pair of electrical relays. The "lock-in" relay locks in contacts that keep the control circuit energized when the push button is released. (The start button is a normally open contact and the stop button is normally closed contact.) Another relay energizes a switch that powers the device that throws the motor starter switch (three sets of contacts for three phase industrial power) in the main power circuit. Large motors use high voltage and experience high in-rush current, making speed important in making and breaking contact. This can be dangerous for personnel and property with manual switches. The "lock in" contacts in the start circuit and the main power contacts for the motor are held engaged by their respective electromagnets until a "stop" or "off" button is pressed, which de-energizes the lock in relay. Commonly interlocks are added to a control circuit. Suppose that the motor in the example is powering machinery that has a critical need for lubrication. In this case an interlock could be added to insure that the oil pump is running before the motor starts. Timers, limit switches and electric eyes are other common elements in control circuits. Solenoid valves are widely used on compressed air or hydraulic fluid for powering actuators on mechanical components. While motors are used to supply continuous rotary motion, actuators are typically a better choice for intermittently creating a limited range of movement for a mechanical component, such as moving various mechanical arms, opening or closing valves, raising heavy press rolls, applying pressure to presses.

Continued in the next blog...

Artificial Intelligence (Part-III)- Developing Artificial Intelligence With Systematic Planning and Learning

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Systematic Knowledgebase with Common Sense are the Corner-Stone of Artificial Intelligence

Today we continue with the third part of our blog on artificial intelligence. Those who have missed our second blog can read it from Here. It will help to connect with this third part of the blog discussing the importance of proper and systematic use of knowledgebase and common sense to develop artificial intelligence.

Knowledge Engineering: The Corner-Stone to Classical Artificial Research

There are some expert systems to collect together accurate knowledge possessed by experts in some narrow domain. In addition, some projects attempt to collect the "fundamental knowledge" known to the average person into a database containing comprehensive knowledge about the world. Among the things a comprehensive fundamental knowledge base would contain are: objects, properties, categories and relations between objects; situations, events, states and time;  causes and effects; knowledge about knowledge and many other, less well researched domains. A depiction of "what exists" is an ontology: the set of objects, relations, approach, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language. The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval, scene analysis, clinical judgement support, knowledge discovery,  and other areas.

Artificial Intelligence: Broad Combination of Common Sense and Knowledgebase

It’s within the grip of common people is to represent the knowledge as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed" or an art authority can take one look at a statue and realize that it is a fictitious. These are non-conscious and sub-symbolic intuitions or tendencies in the human brain. Knowledge like this apprise, guide and provides a context for symbolic, cognizant knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to perform this type of knowledge. The count of atomic facts that the moderate person knows is very huge. Research projects that pursuit to build a complete knowledge base of common-sense knowledge require huge amounts of laborious ontological engineering they must be develop, manually, one complicated concept at a time.

How Planning and Learning is done in Artificial Intelligence?

Multi-agent planning uses the assistance and counteraction of many agents to fulfil a given target. Appearing behaviour such as this is used by evolutionary algorithms and swarm intelligence. In humanistic planning problems, the agent can speculate that it is the only system acting in the world, allowing the agent to be certain of the countercoup of its actions. However, if the agent is not the only actor, then it requires that the agent can acumen under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also appraise its predictions and adapt based on its assessment. Computational learning theory can permit beginner by computational complexity, by sample complexity about the exact amount of data that is required, or by other notions of optimization. In brace learning the agent is rewarded for good feedback and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for running in its problem space. Unsupervised learning is the capability to search patterns in a torrent of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be displayed as learning a function that maps from the written text of an email to one of two categories, "spam" or "not spam".

Intelligent agents must be capable to set targets and achieve them. They need a way to visualize the future a depiction of the state of the world and be able to make predictions about how their actions will change it and be able to make choices that maximize the utility (or "value") of available choices.

An Important Note to Always Keep in Mind 

Artificial Intelligence and the automated technology are one side of the life that always interest and wander us with the new ideas, topics, innovations, products …etc. AI is still not enforce as the films representing it(i.e. intelligent robots), however there are many important tries to reach the level and to challenge in market, like sometimes the robots that they show in TV. Nevertheless, the hidden projects and the advancements in industrial companies. 

Continued in the next blog...

Artificial Intelligence (Part-I)- The Driving Force of Modern Digitalization

Image for representative purpose only.

Introduce Yourself to the World of Artificial Intelligence, a Giant Leap Towards Modern Digitalization

Artificial Intelligence (AI) is the most popular and desirable tool for ground breaking development in almost every field of science and technology. This tool is continuously paving the way for modern digitalization thus providing better performance and greatly reducing human effort. We will present you with a series of blogs to know and explore about this interesting field.

Introducing Artificial Intelligence

Artificial intelligence is a kind of intelligence exhibit by machines in comparison to the natural intelligence (NI) spread out by humans and other animals. In computer science AI research is described as the study of "intelligent agents": any device that recognize its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is enforced when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". Once the President of Future of Life Institute, Mr. Max Tegmark  quoted that-

"Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial".

Starting from the world best AI driven applications up to self-driven car, AI is progressing rapidly. Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term targets of many researchers is to develop general AI. While narrow AI may perform whatever its specific task is, like solving equations, AI would outperform humans at nearly every mental task. The ambit of AI is disputed: as machines become increasingly proficient, tasks considered as requiring "intelligence" are often eliminated from the definition, a method known as the AI effect, initializing to the quip, "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from "artificial intelligence", having become a routine technology. Efficiency generally classified as AI as of 2017 include successfully understanding man’s speech, challenging at the greatest level in strategic game systems, autonomous cars, intelligent routing in content delivery network and military counterfeiting. The field started on the claim that human intelligence "can be so minutely demonstrated that a machine can be made to counterfeit it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by illusion, fiction and philosophy since the end. Some people also consider AI to be an emergency to humanity if it progresses unabatedly. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment. In the twenty-first century, AI techniques have accomplished a resurgence following circumstantial advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to clarify many confronting problems in computer science.

Phrase History

Artificial intelligence was developed as an academic discipline in 1956, and in the years since has accomplished several waves of anticipation, followed by disappointment and the loss of funding. The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which advised that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This acumen, that digital computers can counterfeit any process of formal reasoning, is known as the Church–Turing thesis. Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to contemplate the possibility of building an electronic brain. Turing expected that "if a human could not distinguish between replies from a machine and a human, the machine could be considered “intelligent". The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons". According to Bloomberg's Jack Clark, in the year 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011. He aspect this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and data sets. Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people.

Continued in the next blog...