Astronaut And Space Essays - Apollo Program, United States

Astronaut And Space On May 25, 1961, John F. Kennedy delivered one of the most memorable State of the Union addresses in the history of the United States. ?I believe that this nation should commit itself to achieving the goal, before this decade is out, of landing a man on the Moon and returning him safely to the earth? (http://www.cs.umb.edu/jfklibrary, President John F. Kennedy's Special Message to the Congress on Urgent National Needs). With those words, Kennedy launched a new era of space exploration in the United States. Although the National Aeronautics And Space Administration was created in 1958 by the National Aeronautics and Space Act (http://www.hq.nasa.gov, Key Documents), and the Russians already launched the first satellite into space in 1957, the US was still at a stand still on the subject. What the country needed was a wake-up call, and that is exactly what it got from one of the most celebrated speakers in its history. The new era promised much, but expected little. From USA's struggle to be the dominant world power in the Cold War Era, to the careless depletion of natural resources in the Information Age, space exploration and astronauts were and will be the real keys to the new millennium and beyond. Before looking into the future, or even evaluating the present, one must look in detail at the history of the space project. The missions that gave scientists and engineers the necessary data and experience to make new, safer, more reliable and intricate equipment were launched long before there was realistic talk of sending probes to Mars. The astronauts that helped shape the training programs, took the beatings of primitive flight tests, and died in order to serve their country were born before World War II. And even the Russian Space Program was crucial to what the space program is today. It fueled competition, and provided more resources for American engineers. Until Apollo 11, they were ahead of the Americans in almost everyway, with their launch of Sputnik, a unmanned satellite in 1957, and their countless firsts in orbiting and space walks. Yuri Gagarin was the first man in space. Although most of the missions that have been launched have been important in their own ways, some missions just stand out, whether it was the first step on the Moon, or the first mission to Mars. NASA's first high profile program was Project Mercury, an effort to learn if humans could survive in space. It was the prelude to the later missions, and it gave NASA the necessary data to build better, and more comfortable ships for humans to stay in space for extended periods of time. The first launch of the Mercury program was the LJ-1 on August 21, 1959. At thirty-five minutes before launch, evacuation of the area had been proceeding on schedule. Suddenly, half an hour before launch-time, an explosive flash occurred. When the smoke cleared it was evident that only the capsule-and-tower combination had been launched, on a trajectory similar to an off-the-pad abort (http://www.ksc.nasa.gov, Mercury: LJ-1). The first mildly successful spacecraft launch occurred September 9, 1959. Although t he BJ-1 ship experienced some problems, and the timing on some of the separation procedures was off, the capsule made it back to earth some seven hours after lift-off. The capsule orbited the earth for approximately thirteen minutes (Mercury: BJ-1). Mercury mission MA-5 was the first to carry live organisms into sub-orbit. Although Enos - a chimpanzee, was not a perfect substitute for a human, he served as a good test for the environmental controls of the capsule. He orbited the earth in total weightlessness for over three hours and upon landing was in perfect physical condition (Mercury: MA-5). On May 5, 1961, Freedom 7 was the first launch to carry humans into space. Alan B. Shepard, Jr. was the only crewmember, and the successful mission lasted for over 15 minutes (Mercury: MR-3). More manned flights from the Mercury series followed, highlighted by the Friendship 7, where on February 20, 1962, John Glenn was the first American in actual orbit, and he orbited the earth three times for a little under five hours (Mercury: MA-6). The last mission from the Mercury project came on May

Teetotaller - Definition and Origin

Teetotaller - Definition and Origin Definition: A teetotaller is someone who totally abstains from liquor. In the 19th century, the Preston Temperance Society in England and, later, the American Temperance Union encouraged a pledge of abstinence from intoxicating liquor, as part of the temperance movement. Those who had signed the pledge were asked to use a T with their signature to mean total abstinence. The T plus the total led to those whod signed the pledge being called T-totallers or teetotallers. The term was in use as early as 1836 when an explanation of it as meaning total abstainer appeared in print. From there, the term came to be used more generally, for anyone who voluntarily committed to abstinence, or simply for a nondrinker. The Pledge The pledge of temperance from the Preston Temperance Society (in Preston, England) read: We agree to abstain from all liquors of an intoxicating quality whether ale, porter, wine or ardent spirits, except as medicine. Also Known As: Abstainer, dry, nondrinker, prohibitionist Other words for teetotalism:  Abstinence, temperance,  abstemiousness, on the wagon, dry, sober. Alternate Spellings: t-totaller, teetotaler Examples: First Lady Lucy Hayes, wife of President Rutherford B. Hayes, was known as Lemonade Lucy because, as a teetotaller, she did not serve liquor in the White House. Henry Ford required a teetotaller pledge for those he hired in his new auto production industry, to promote better productivity and workplace safety. Learn more about how teetotallism fit into the more general movement to limit or ban the use of alcoholic beverages: Temperance Movement and Prohibition Timeline Image: the image included is an example of the Victorian era pledge, complete with very Victorian floral embellishment. Religious groups that require or encourage abstinence from the use of alcoholic beverages: Assembly of God,  Bahai, Christian Science,  Islam, Jainism, The Church of Jesus Christ of Latter-day Saints (LDS. also known as the Mormon Church), Seventh-day Adventist Church, the Church of Christ, Sikhism, Salvation Army.   Also, some Hindu and Buddhist sects, and some Mennonite and Pentecostal groups. Methodists in English and American history often taught abstinence but rarely do that currently. In the Victorian era, many in both the Evangelical and Unitarian movements taught at least restraint, if not temperance and teetotalling. Most of those religions that prohibit alcohol do so on the grounds that it is harmful, that it inhibits mindfulness, or can easily lead to unethical behavior. Some famous women teetotallers: In history, women becoming teetotallers was often an expression of religious values, or was based on general social reform principles.   In the modern world, some women become teetotallers for such reasons, and others because of a past history of alcoholism or alcohol abuse. Tyra Banks: a model and actress.Susan Boyle: singer.Pearl S. Buck: writer, won Nobel Prize for Literature, 1938.Faye Dunaway: actress.Janeane Garofalo: actress.Kathy Griffin: comedian.Elisabeth Hasselbeck: television personality.Jennifer Hudson: singer.Carrie Nation: temperance activist.Kelly Osbourne: actress.Marie Osmond: singer.Natalie Portman: actress.Anna Quindlen: writer.Christina Ricci: actress.Anne Rice: writer.Linda Rondstadt: singer.Sarah Silverman: comedian, actress and writer.Jada Pinkett Smith: actress.Lucy Stone: womens rights activist.Mae West: actress.  Frances Willard: temperance reformer.

We are Seven(Lyrical Ballads) Term Paper Example | Topics and Well Written Essays - 2000 words

We are Seven(Lyrical Ballads) - Term Paper Example Wordsworth wrote the poem ‘we are seven’ at the age of 18 when he was going on a journey with his best companion Coleridge. ‘Wordsworth’s most deeply held conviction is his belief that the scope for poetry was often best provided by memory.’ As we also observe in this particular poem that the little girl that he talks about is actually someone he had met on his journey in a similar setting. As stated, ‘We Are Seven was inspired by a little girl he met in the area of Goodrich Castle.’ The poem comprises of sixteen four lined stanzas with the exception of the last stanza that consists of five lines. The rhyme scheme of the poem is ABAB. The poem is in the form of a conversation between a stranger and a little girl. The technique of writing the poem in a dialogue form enhances the beauty of the poem as it helps in retaining the interest of the reader, as he is curious about the end result of the conversation. The title of the poem hints about a sense of security that is related to the knowledge of having strong family bonding as observed from the word ‘We’. It has a tone of finality to it. The two letter word hold a lot of meaning as it also indicates the element of stubbornness and conviction of belief that nothing can change the reality i.e. we are seven. Number ‘seven’ can in itself be a reference to ‘Shakespeare’s seven stages’ of a man’s life as the readers later observes that this poem also draws a contrast between the perspective of life from the eyes of a child and an adult and shows the manner in which a person loses his idealism in order to grow up. The poem is basically about a conversation between a man and a little girl about her family and loved ones. The stranger inquires about her brothers and sisters and the girl replies that we are seven. This reply leads to the formation of the whole conversation that takes place in the poem.

International Business Assignment Example | Topics and Well Written Essays - 2750 words

International Business - Assignment Example Currently, Coca Cola faces competition from other soft drink producers that is not all that substantial but the threat from substitute products is growing. These are only but a few o the challenges that the company may have to face – both now and in the future. The soft drink industry may be strong for now but this does not mean the consumers will remain marred to it. Coca Cola should pursue a strategy that will reduce the overall cost of its products. This strategy will give Coca Cola a much competitive advantage over its rivals who have already adapted methods of lowering the cost of their products. This is one of the many recommendations that the company should adopt if it has to enjoy continued success in the international market. International Business is the terminology that generally describes the business activities that occur across the boundaries of countries for both profit making and political reasons (Daniels & Radebaugh, 2007). This article will explore one such company doing business internationally namely the Coca Cola company, and identify a few significant issues related to its international operations. Coca Cola, which is the symbolic American soda company, is among the companies that have operated internationally for a lengthy time period, having first gone international in the year 1902. It is currently a market leader in terms of soft drinks sales worldwide given that its products sell up to one and a half billion servings on a daily basis. The company can also boast of having the world’s largest production, distribution and marketing system of syrup and non-alcoholic beverage which is in fact double in size of its nearest competitor. For numerous reasons, the Coca Cola Company holds the belief that it will maintain its international growth. Among these reasons are its rising disposable income and the global

An investigation into the theory and practice of management skills Essay

An investigation into the theory and practice of management skills development in the hospitality and tourism industries - Essay Example Thankfully my research and academic proficiency led me to applying in Marriot International among other companies for a management trainee post and getting a positive response from the company. Marriot International is the third largest chain of hotels in the world by employees and revenues but what struck me most about the company was that it consistently got selected as one of the five most ethical companies in an annual review carried out by a rating and analysis firm. The company is based in the United States but has a large setup in the United Kingdom and offers a management trainee program or as the company calls it, a leadership development program, in 30 countries of the world. I got selected in this program that goes by the name of Voyage and conveyed my consent for joining appropriately quickly. Now I am an inductee into the program in its UK chapter and it is early days for me as a trainee of event management, a field of profession I chose to enter after long and careful deliberation. I think that the most prominent feature of event management that is not as important in other types of management is time management which I think is one of the best qualities a person can possess. Also if an event and the planning leading up to it are considered a complete project, I shall get more experience of handling whole projects than I would have gotten in any other field of corporate management. Finally, I have taken this route because I would like t o permanently leave the status of employee one day and establish my own company in the event management business. The first skill that I think I will be requiring to acquire to progress on this career path is keeping my fitness level high. By fitness I do also mean personal health care as well. I have honestly concluded from what I have come to know so far about the job of event management for large companies that it is a tenuous job; it is both physically and

Introduction To Dc Dc Converter Engineering Essay

Introduction To Dc Dc Converter Engineering Essay ABSTRACT A bidirectional dc-dc converter is used for dc-dc power conversion applications. The power converter includes two full bridge converters This Bidirectional dc-dc converter is best for electrical vehicle applications. A bidirectional, isolated topology is proposed in consideration of the differing fuel cell characteristics from traditional chemical-power battery and safety requirements. The topology proposed in the paper has advantages of simple circuit with soft switching implementation without additional devices, high efficiency and simple control. INTRODUCTION TO DC-DC CONVERTER: DC-DC converters are devices which change one level of direct current/ voltage to another (either higher or lower) level. They are primarily of use in battery-powered appliances and machines which possess numerous sub circuits, each requiring different levels of voltages. A DC-DC converter enables such equipment to be powered by batteries of a single level of voltage, preventing the need to use numerous batteries with varying voltages to power each individual component.   1.1. BUCK-BOOST CONVERTER Fig. 1: schematic for buck-boost converter With continuous conduction for the Buck-Boost converter Vx =Vin when the transistor is ON. When the transistor is OFF the inductor in trying to maintain the current in the same direction reverses its polarity as a result of which the diode is forward biased and Vx =Vo. For zero net current change over a period the average voltage across the inductor is zero. Fig. 9: Waveforms for buck-boost converter Vin ton + Vo toff = 0 which gives the voltage ratio and the corresponding current Since the duty ratio D is between 0 and 1 the output voltage can vary between lower or higher than the input voltage in magnitude. The negative sign indicates a reversal of sense of the output voltage.   CONVERTER COMPARISON The voltage ratios achievable by the DC-DC converters is summarised in Fig. 10.We can notice that only the buck converter shows a linear relationship between the control (duty ratio) and output voltage. The buck-boost can reduce or increase the voltage ratio with unit gain for a duty ratio of 50%. Fig. 10: Comparison of Voltage ratio 1.3 BI-DIRECTIONAL DC-TO-DC CONVERTER A DC-DC converter which can be operated alternately as a step-up converter in a first direction of energy flow and as a step-down converter in a second direction of energy flow is disclosed. Potential isolation between the low-voltage side and the high-voltage side of the converter is achieved by a magnetic compound unit, which has not only a transformer function but also an energy storage function. The converter operates as a push-pull converter in both directions of energy flow. The DC-DC converter can be used for example in motor vehicles with an electric drive fed by fuel cells. A bi-directional converter for converting voltage bi-directionally between a high voltage bus and a low voltage bus, comprising a switching converter connected across the high voltage bus, the switching converter comprising first and second switching modules connected in series across the high voltage bus, a switched node disposed between the switching modules being coupled to an inductor, the inductor connected to a first capacitor, the connection between the inductor and the first capacitor comprising a mid-voltage bus, the first and second switching modules being controllable so that the switching converter can be operated as a buck converter or a boost converter depending upon the direction of conversion from the high voltage bus to the low voltage bus or vice versa; the mid-voltage bus being coupled to a first full bridge switching circuit comprising two pairs of series connected switches with switched nodes between each of the pairs of switches being connected across a first wi nding of a transformer having a preset turns ratio; and a second full bridge switching circuit comprising two pairs of series connected switches with switched nodes between each of the pairs of switches being connected across a second winding of the transformer, the second full bridge switching circuit being coupled to a second capacitor comprising a low voltage node. 1.3.3 WORKING OF DC-DC Converters In its simplest form, a DC-DC converter simply uses resistors as needed to break up the flow of incoming energy this is called linear conversion. However, linear conversion is a wasteful process which unnecessarily dissipates energy and can lead to overheating. A more complex, but more efficient, manner of DC-DC conversion is switched-mode conversion, which operates by storing power, switching off the flow of current, and restoring it as needed to provide a steadily modulated flow of electricity corresponding to the circuits requirements. This is far less wasteful than linear conversion, saving up to 95% of otherwise wasted energy. 1.3.2 BIDIRECTIONAL DC-DC CONVERTERS TOPOLOGIES There are many circuit topologies for bidirectional dc-dc converter. Some of them are Non isolated (Without transformer): Full bridge bidirectional dc-dc converter (shown in fig) Half bridge bidirectional dc-dc converter II. Isolated (with transformer): Full bridge bidirectional dc-dc converter ( shown in fig) Half bridge bidirectional dc-dc converter 1.3 NON-ISOLATED BIDIRECTIONAL DC-DC CONVERTER: Fig2: Full bridge bidirectional dc-dc converter Fig 17 shows a basic circuit diagram of a full bridge bidirectional DC-DC converter. It has interleaved operation for both boost and buck modes It has smaller passive components It has less battery ripple current 1.3.2.2 ISOLATED BIDIRECTIONAL DC-DC CONVERTER (PROPOSED CONVERTER): Fig18: lv-side current source and hv-side voltage source Fig 18 shows the circuit diagram of an Isolated DC-DC converter. This converter has the following features Simple voltage clamp circuit implementation Simple transformer winding structure and low turns ratio High choke ripple frequency (2fs) Start up problem will be present in this circuit 1.4 SEMICONDUCTOR SWITCHING: Semi conductor switching are of two types. They are 1. Hard Switching 2. Soft Switching 1.4.2 SOFT SWITCHING More recently, new power conversion topologies have been developed which dramatically reduce the power dissipated by With soft switching techniques, reduction in wasted power will often improve the efficiency of a unit by more than 2%. While this does not sound significant, it can account for a saving of more than 20 W in a 1000 W power supply. This 20 W is power that would have been dissipated by the main power transistors, the most critical and most heavily stressed semi-conductors in any switch mode power supply. Reducing the power here lowers their junction temperature, giving increased thermal operating margins and, hence, a longer life for the power supply. Not only does a soft switching power supply generate significantly less electrical noise, it achieves greater efficiency, longer mean time between failures (MTBF), and higher immunity to the effects of other equipment operating nearby. It is desirable for power converters to have high efficiencies and high power densities. Packaging and cost limitations require that the converter have a small physical size and weight. Power density and electrical performance are dependent on the switching frequency as it determines the values of the reactive components in the converter. Thus, high frequency operation of the converter is highly desired. However, operation at high frequency results in higher switching losses and higher switching stresses caused by the circuit parasitics (stray inductance, junction capacitance). The circuit topology of the proposed bidirectional isolated converter is shown in Fig. According to the power  ¬Ã¢â‚¬Å¡ow directions, there are two operation modes for the proposed converter. When power  ¬Ã¢â‚¬Å¡ows from the low-voltage side (LVS) to the high-voltage side (HVS), the circuit operates in boost mode to draw energy from the battery. In the other power  ¬Ã¢â‚¬Å¡ow direction, the circuit operates in buck mode to recharge the battery from the high-voltage dc bus. Based on the symbols and signal polarities introduced in Fig. 2, the theoretical waveforms of the two operation modes are shown in Fig. (a) and (b), respectively. Fig42: Theoretical waveform under (a) boost and (b) buck operation Boost Mode (Discharging Mode) Operation When the dc bus voltage in the HVS is not at the desired high level, such as during a cold start, the power drawn from the low-voltage battery flows into the high-voltage dc bus. During this mode, the proposed converter is operated as a current-fed circuit to boost the HVS bus voltage. The LVS switches Q1, Q4 and Q2, Q3 operate at asymmetrical duty ratios and 1- which require a short overlapping conduction interval. Referring to the equivalent circuits for the boost mode operation in Fig. 43, the detailed operating principle can be explained as follows. Although the LVS switches subject to higher voltage stress, this is an advantage because the battery voltage is low. Because the overlapping interval for the LVS switches Q1, Q4 and Q2, Q3 is very short, the LVS transformer current flows through only one LVS switch at most time. Thus, the conduction losses for Q1, Q4 and Q2, Q4 can be greatly reduced to improve the conversion efficiency. Moreover, the LVS circuit produces a relatively ripple free battery current that is desirable for the low voltage battery. The voltage transfer ratio Mboost for the boost mode operation for the proposed dc-dc converter can be derived from the volt-second balance condition across the inductor L1 represented by (7). The current stresses of the inductor windings can be also determined as (6).The inductances of the power inductor L1 can be determined for their given peak-to-peak current ripples, ΆI1 Where ÃŽÂ » (%) is the ripple percentage of the inductor currents IL1 B. Buck Mode (Charging Mode) Operation Different from the traditional electric vehicle driving system, the fuel cell powered system needs an additional energy storage device to absorb the feedback power from the electric machine. This energy storage device may be a lead-acid battery as shown in Fig44 . The proposed circuit works in buck mode to recharge the battery from high-voltage dc bus. During this mode, the proposed converter is operated as an asymmetrical half bridge circuit with synchronous rectification current doubler to recharge the battery from high-voltage dc bus. The HVS switches Q5, Q8 and Q6, Q7 operate at asymmetrical duty ratios and 1- which require short and well-defined dead time between the conduction intervals. Referring to the equivalent circuits in Fig. , the detailed operating principle of this mode can be explained as follows. Fig44: modes of operation in buck mode While the LVS switches, Q1, Q4 and Q2, Q3, share unequal voltage and current stresses, the HVS switches, Q5, Q8 and Q6, Q7, share equal voltage stresses as (8). Then the current stresses of the HVS switches can be found as DESIGN CONSIDERATIONS FOR KEY COMPONENTS To verify the feasibility of the proposed scheme, a 2-kW laboratory prototype operated at 20 kHz was built. The simulation and experimental results will be shown and discussed in the next section. The LVS of the design example was connected to a 12-V lead-acid battery whose terminal voltage could swing from 10-15 V. The nominal voltage on the HVS dc bus was designed to 300 V, with an operating range from 150-400 V. The design considerations Based on (5), the turn-ratio selection of transformer can be calculated as (15). The HVS device ratings can then be calculated using (8)-(10) as follows: B. Power Inductors Let the peak-to-peak current ripples be 20% of the inductor currents under full power. The current rating and the inductance of the power inductor L1 can be determined using (6)- (7) as follows: Because of the ripple cancellation on the battery current, a larger ripple current in inductor L1 and can be allowed in practical applications. Thus, the inductance and the size of the inductors L1 might be smaller. To verify the theoretical operating principles, a 2-kW design example was simulated by using MATLAB. There is a good agreement between the simulation results and theoretical analysis. In this research, a 2-kW laboratory prototype was implemented and tested to evaluate the performance of the proposed bidirectional isolated dc-dc converter.. The ripple cancellation between two inductor currents can be observed. This is desirable for a low-voltage battery. 7.1 BOOST OPERATION FOR BIDIRECTIONAL DC-DC CONVERTER CONVENTIONAL CIRCUIT FOR BOOST MODE 7.3 RESULTANT WAVE FORM: 7.3.1 BOOST OPERATION Input and Output waveform: Fig 48 CONVENTIONAL CIRCUIT FOR BUCK MODE Fig47 7.4 RESULTANT WAVEFORM FOR BUCK OPERATION : Input and Output voltage waveform Fig 49 Proposed Bidirectional DC-DC converter Input and Output Voltage Waveforms: Fig50 Fig 51 Inductor Current Waveforms: Fig52 CONCLUSION A soft-switched isolated bidirectional dc-dc converter has been implemented in this paper. The operation, analysis, features and design consideration were illustrated. Simulation and experimental results for the 45W, 20 kHz prototype was shown as per principle. It is shown that ZVS in either direction of power flow is achieved with no lossy components involved As results, advantages of the new circuit including ZVS with full load range, decreased device count, high efficiency (measured more than 94% at rated power), and low cost as well as less control and accessory power needs, make the proposed converter very promising for medium power applications with high power density.

Markov Analysis

1. Describe the internal labor market of the company in terms of job stability (staying in same job), promotion paths and rates, transfer paths and rates, demotion paths and rates, and turnover (exit) rates. Transition Probabilities for 2010-2011 Sales, Full-time 1. 50% stayed the same 2. 10% transferred to part-time status 3. 5% were promoted to Asst. Sales Mgr. 4. 0% were promoted to Regional Sales Mgr. 5. 35% left the organization Sales, Part-time 1. 5% transferred to full-time status . 60% stayed the same 3. 10% were promoted to Asst. Sales Mgr. 4. 0% were promoted to Regional Sales Mgr. 5. 25% left the organization Asst. Sales Mgr. 1. 5% were demoted to full-time sales 2. 0% were demoted to part-time sales 3. 80% stayed the same 4. 10% were promoted to Regional Sales Mgr. 5. 5% left the organization Region Sales Mgr. 1. 0% transferred to another job 2. 70% Stayed the same 3. 30% left the organization 2. Forecast the numbers available in each job category in 2013.Job 2012 Categor y Employees SF SP ASM RSM TOTAL EXIT Sales Full-time (SF) 500 250 50 25 0 325 175 Sales Part-time (SP) 150 7. 5 90 15 0 112. 5 37. 5 Asst. Sales Mgr. (ASM) 50 2. 5 0 40 0 47. 2. 5 Region Sales Mgr. (RSM) 30 0 0 0 21 21 9 Work Sales Full-Time (SFT) Sales Part-Time(SPT) Asst. Sales Mgr(ASM) Reg Sales Mgr a. ) . 50 x 500 = 250 b. ) . 05 x 150 = 7. 5 c. ) . 05 x 50 = 2. 5 d. ) . 70 x 30 = 21 . 10 x 500 = 50 . 60 x 150 = 90 . 80 x 50 = 40 . 30 x 30 = 9 . 05 x 500 = 25 . 0 x 150 = 15 . 10 x 50 = 5 . 35 x 500 = 175 . 25 x 150 = 37. 5 . 05 x 50 = 2. 5 3. Indicate potential limitations to your forecasts. A. ) As a rule, it is desirable to have 20 or more employees in each job category/level. Some of the cells contain less than 20 cases. So the estimates based on these figure can yield unstable estimates of future availabilities. B. ) There is a possibility of results not being accurate due to multiple moves by employees.The analysis cannot detect multiple moves by employees between T and T+1 ; it only classifies employees and counts their movement according to their beginning (T) and ending (T+1) job category/level, ignoring any intermittent moves. C. ) The forecast assumes that all employees in a job category/level have an equal probability of movement. This is unrealistic because organizations take many factors into account. Because of these factors, the probabilities of movement may vary among specific employees.

Comparing Ireland and Denmark ECC Essay

Choose two countries and compare their approaches to early childhood care and education. This essay will introduce and compare approaches to early childhood care and education in Denmark and Ireland. It will specifically focus on comparing the pedagogical approach, curriculum content and the inclusion of ethnic minority children aged 0-6 years attending early childhood settings in both countries. Provision of Services As one of the oldest nations within Europe, Denmark has made the welfare of families with children top priority within government. Under their Social Services Act, matters’ relating to the care and education of children is broken down. The Ministry of Social Services is responsible for day care facilities – dagtilbud, while the Ministry of Education is responsible for pre-school services – bornehaveklasse, primary and lower secondary and afterschool services and forest kindergartens are also available. These services are based on the rights of children outlined in the UN Convention on the Rights of the Child (OECD, 2000). In Denmark children are not required to enter the primary school sector until they are seven years old. In Ireland, there has been very little funding and resources into the development of childcare provisions with many working families relying on family members or child-minders filling the gap. Full day care exists at a cost and sessional services either morning or afternoon are in operation. In 2010, the Office of the Minister for Children and Youth Affairs introduced a voluntary, universal free pre-school year for children aged three years and two months and less than four years and seven months. For the year 2010/11, over 94% of eligible children were participating in the scheme (Dept. f Education & Skills, 2011). While it is compulsory for children to enter the primary education system at six years old, it is relevant to note that many children enter the system in the September, following their fourth birthday due to the lack of childcare provisions available. The Irish welfare state seems reliant on offering cash benefits rather than services especially for the youngest group of children 0-2 years (OECD, 2010). Pedagogy French, 2007 defines pedagogy as the practice or craft of teaching in the interactive process between teachers and learners and the learning nvironment which includes family and the community. Under Siolta, the national quality framework it is defined as the range of interactions to support the holistic development of children by embracing both care and education within settings. Denmark has a strong historical background in relation to the training of early care and education practitioners. The first training programme was implemented in 1885 for those working from the frobelian approach in education; this formed a basis for the two year study programme implemented in 1904. In 1992, an integrated training system was established to combine theory with practice. Those training to be social pedagogues complete three and a half year degree programme learning theoretical, culture based subjects and activity based subjects with stints in placement (OECD,2000). Ireland in comparison has yet to regulate for proper qualifications within the sector with only those working with pre-primary classes having the appropriate qualification – bachelor of education. In order for the pre-school year to be implemented room leaders must have a minimum qualification of a Level 5 major award in ECCE (Dept. of Education &Skills, 2011). Curriculum French, 2007, states that a curriculum being implemented in settings for children’s learning should contain a ‘body of knowledge with a clear set of goals and objectives’. The National Council for Curriculum and Assessment (NCCA) in 2004 wrote that curriculum refers to â€Å"all learning experiences, whether formal or informal, planned or unplanned, which contribute to a child’s development†. Denmark’s curriculum for children aged 0-6 years is predominately play based and incorporates the holistic development of children. Children work in partnership with the pedagogues throughout daily life. The main aims of the curriculum are to ensure children learn and experience the natural environment while gaining a true understanding of the Danish and other cultures represented. In 2004, a new law of pedagogical curriculum to â€Å"support, lead and challenge the learning of children† was introduced (Starting Strong 2, 2006). This outlined that all centres had the responsibility to outline their own curriculums with the cooperation of staff and passed by the board of parents and local authorities, the aspect of learning was not to become too structured. Six dimensions of aims as themes are to be represented within the curriculum. These include personal competences, social competences, language, body and movement, nature and nature phenomena and cultural forms of expression and values (Brostrom, 2006).

Image Denoising Based on Soft Computing Techniques

Image Denoising Based on Soft Computing Techniques Free Online Research Papers Image Denoising Based on Soft Computing Techniques Abstract: Image Denoising is one of the existing problems in research area. This paper presents an interactive algorithm for image Denoising and segmentation. This paper explains the task of segmenting any given color image using soft computing techniques. The segmentation techniques used are Fuzzy Clustering (FC), Fuzzy C Means (FCM) clustering and Convolutional Networks (CN). After the image is segmented, the noise can be removed by using bilateral filtering. The denoised images are compared using image quality metrics. The image quality metrics are Peak Signal to Noise Ratio (PSNR), and Mean Average Error (MAE). The time taken for Denoising is also used as a comparison parameter. The techniques have been tested with images of different size and resolution and the results are proven to be better than the existing state-of-art algorithms. Keywords: Denoising, segmentation, Fuzzy clustering, Fuzzy C Means (FCM), Convolutional Networks (CN), bilateral filtering. 1. Introduction Images are often corrupted by random variations in intensity values, called noise, either because of the data acquisition process, or because of occurring phenomena at scene of interest. The goal of image denoising methods is to recover the original image (better quality image) from a noisy one, in order to perform, in an easier and more accurate way, an image processing task as image segmentation. In computer vision literature, various methods dealing with segmentation, and feature extraction are discussed, which can be broadly grouped into region based techniques, edge based techniques, hybrid methods which combine edge and region methods, and so on. However, because of the variety and complexity of images, robust and efficient segmentation algorithm on color images is still a very challenging task and fully automatic segmentation procedures are far from satisfying in practical situations. This paper explains the task of classifying each pixel in an image into one of a discrete level of color classes using three main soft computing techniques, namely Fuzzy clustering, Fuzzy C Means, and Convolutional networks. The results obtained by soft computing techniques are compared with traditional hard c means technique. The results are found to be more accurate and reliable than the traditional method. The rest of this paper is explained as follows: in Section 2, the three types of soft computing techniques are explained, the use of bilateral filtering is explained in Section 3, segmentation based Denoising is explained in Section 4, the experimental results are shown in Section 5, and the conclusion is in Section 6. 2. Soft Computing Techniques: Extracting information from an image is referred to as image analysis. Image segmentation is a preliminary step in most automatic pictorial pattern recognition and scene analysis problems. It is one of the most difficult tasks in image processing. Image segmentation is the process of partitioning a digital image into multiple regions or clusters. Each region is made up of sets of pixels. Image segmentation simplifies and changes the representation of an image. i.e. the image is transferred into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects of interest and boundaries like lines, curves in an image. The pixels of a color image are represented as vectors. Each pixel is represented a triplet containing red, green, blue spectral values at that position. The RGB color model is shown in fig.1. This is based on Cartesian coordinate system. A color expressed by an RGB vector is said to be a color represented in RGB space. Fig.1.RGB Color Model. RGB color representation is one of the numbers of color models. RGB color model is chosen for image segmentation due to its simplicity hence the fast processing speed that could be achieved [5]. Image segmentation refers to the process of dividing the image into connected regions where pixels of a region share a common property. For color images the common property is usually considered is the red: green: blue, color ratio. This ratio must be reasonably constant over the region. The color ratio does not have smoothly varying values when the pixel intensity is low. So color image segmentation based on color ratio requires that the intensity of the image must be above a threshold value. So instead of segmentation based on color ratio other techniques have been evolved. The requirements of good color image segmentation are as follows. A single region in a segmented image should not contain significantly different colors and a connected region containing same color should not have more than one label. All significant pixels should belong to the same labeled region. The intensity of a region should be reasonably uniform. Several image segmentation techniques have been suggested for gray scale images. In this paper we suggest the neural network approach for color images. 2.1.Fuzzy Clustering In non-fuzzy or hard clustering, data is divided into crisp clusters, where each data point belongs to exactly one cluster. In fuzzy clustering, the data points can belong to more than one cluster[3], and associated with each of the points are membership grades that indicate the degree to which the data points belong to the different clusters. Fuzzy clustering belongs to the group of soft computing techniques (which include neural nets, fuzzy systems, and genetic algorithms). In real applications there is very often no sharp boundary between clusters so that fuzzy clustering is often better suited for the data. Membership degrees between zero and one are used in fuzzy clustering [6] instead of crisp assignments of the data to clusters. The resulting data partition improves data understanding and reveals its internal structure. Partition clustering algorithms divide up a data set into clusters or classes, where similar data objects are assigned to the same cluster whereas dissimilar data objects should belong to different clusters. Areas of application of fuzzy cluster analysis include data analysis, pattern recognition, and image segmentation. The detection of special geometrical shapes like circles and ellipses can be achieved by so-called shell clustering algorithms. 2.2. Fuzzy C Means The most prominent algorithm is the FCM or Fuzzy C Means algorithm. The fuzzy C means algorithm was proposed as an improvement of the classic Hard C-Means clustering algorithm The FCM algorithm receives the data or the sample space, an n x m matrix where n is the number of data and m is the number of parameters. The number of clusters c, the assumption partition matrix U, the convergence value E all must be given to the algorithm. The assumption partition matrix has c number of rows and n number of columns and contains values from 0 to 1. The sum of every column has to be 1. The first step is to calculate the cluster centers. This is a matrix v of dimension c rows with m columns. The second step is to calculate the distance matrix D. The distance matrix constitutes the Euclidean distance between every pixel and every cluster center. This is a matrix with c rows and n columns. From the distance matrix the partition matrix U is calculated. If the difference between the initial partitio n matrix and the calculated partition matrix is greater than the convergence value then the entire process from calculating the cluster centers to the final partition matrix. The final partition matrix is taken and is used for reconstructing the image. Let us assume as a fuzzy C-Means Functional, (1) where ? = { x k | k â‚ ¬ [1,n]} is a training set containing unlabeled samples = { y j | j â‚ ¬ [1,c]} } is the set of centers of clusters; E j (x k) is a dissimilarity measure (distance or cost) between the sample x k and the center y j of a specific cluster j;U = [u jk] is the c x n fuzzy c-partition matrix, containing the membership values of all samples in all clusters; m â‚ ¬ (1, ?) is a control parameter of fuzziness. The clustering problem can be defined as the minimization of J m with respect to Y, under the probabilistic constraint: (2) The Fuzzy C-Means (FCM) algorithm consists in the iteration of the following formulas: for all j (3) And (4) ? ? ? 1,? if E j (x k) = 0 and u jk = 0 ? l ? j where, in the case of the Euclidean space: E j = xk ? y j 2 (5) It is worth noting that if one chooses m = 1 the fuzzy C-Means Functional J m (Eq. 1) reduces to the expectation of the global error (which we denote as ): (6) and the FCM algorithm becomes the classic Hard C-Means algorithm. 2.3. Convolutional Networks A Convolutional network is an alternating sequence of linear filtering and nonlinear transformation operations. The input and output layers include one or more images, while intermediate layers contain â€Å"hidden units with images called feature maps that are the internal computations of the algorithm. The activity of feature map a in layer k is given by (7) where Ik-1;b are feature maps that provide input to Ik;a, and denotes the convolution operation. The function f is the sigmoid f(x) = 1= (1 + e -x) and ?k;a is a bias parameter. We restrict our experiments to monochrome images and hence the networks contain a single image in the input layer. It is straightforward to extend this approach to color images by assuming an input layer with multiple images (e.g., RGB color channels). For numerical reasons, it is preferable to use input and target values in the range of 0 to 1, and hence the 8-bit integer intensity values of the dataset (values from 0 to 255) were normalized to lie between 0 and 1. We also explicitly encode the border of the image by padding an area surrounding the image with values of -1. 3. Bilateral Filtering: The idea underlying bilateral filtering is to do in the range of an image what traditional filters do in its domain. Two pixels can be close to one another, that is, occupy nearby spatial location, or they can be similar to one another, that is, have nearby values, possibly in a perceptually meaningful fashion. Closeness refers to vicinity in the domain, Similarity to vicinity in the range. Traditional filtering is domain filtering, and enforces closeness by weighing pixel values with coefficients that fall off with distance. Similarly, we define range filtering, which averages image values with weights that decay with dissimilarity. Range filters are nonlinear because their weights depend on image intensity or color. Computationally, they are no more complex than standard nonseparable filters. Spatial locality is still an essential notion. In fact, we show that range filtering by it selfmerely distorts an image’s color map. We then combine range and domain filtering, and show that the combination is much more interesting. We denote the combined filtering as bilateral filtering. Since bilateral filters assume an explicit notion of distance in the domain and in the range of the image function, they can be applied to any function for which these two distances can be defined. In particular, bilateral filters can be applied to color images just as easily as they are applied to black-and-white ones. The CIE-Lab color space [16] endows the space of colors with a perceptually meaningful measure of color similarity, in which short Euclidean distances correlate strongly with human color discrimination performance [16]. Thus, if we use this metric in our bilateral filter, images are smoothed and edges are preserved in a way that is tuned to human performance. Only perceptually similar colors are averaged together, and only perceptually visible edges are preserved. 4. Self Estimation Algorithm and Parameter Settings: If the number of clusters is manually specified, the segmentation may not be effective. Hence there must be a system to calculate the robust number of clusters. A method has been suggested for automatically finding no. of clusters with K means clustering [7]. That algorithm is modified for finding no. of clusters in our work. The self estimation algorithm used for fuzzy clustering techniques finds the Euclidean distance between the different cluster centers. If the maximum Euclidean distance between the cluster centers is greater than the specified value, then the number of cluster centers is increased by one else the clusters are merged.. The self estimation algorithm for neural network finds the difference between the weight vectors. If the difference between the weight vectors is greater than the specified value, then the number of cluster centers is increased by one else the clusters are merged. 4.1. Algorithm Step 0: Initialize weights wij.Set topological neighborhood parameters with its radius as Set learning rate parameters. Step 1: While stopping condition are false, do steps 2 6 Step 2: for each input vector x, do steps 3 – 5 Step 3: For each j, compute: D(j) = ?i (wij – xi)2 Step 4: Find index J such that D(J) is a minimum Step 5: For all units j within a specified neighborhood of J, and for all i: wij(new) = ij(old) + ?[xi – wij(old)] Step 6: Update learning rate The learning rate ? is a gradually decreasing function of training epochs. The formation of the competitive occurs in two phases. In the first phase the initial formation of the correct order takes place. In the second phase the final convergence. The second phase takes much longer than the first and requires a smaller value for the learning rate. Random values may be assigned for the initial weights. If some information is available concerning the distribution of clusters that might be appropriate for a particular problem, the initial weights can be taken to reflect that prior knowledge. 4.2. Parameter Settings: For the bilateral filtering part of the proposed method, we set the parameters as follows: Bilateral filtering with parameters ?d = 3 pixels and ?r = 50 intensity values is applied to the image in figure 3 (a) to yield the image in figure 3 (b). Notice that most of the fine texture has been filtered away, and yet all contours are as crisp as in the original image. Figure 3 (c) shows a detail of figure 3 (a), and figure 3 (d) shows the corresponding filtered version. The two onions have assumed a graphics-like appearance, and the fine texture has gone. However, the overall shading is preserved, because it is well within the band of the domain filter and is almost unaffected by the range filter. Also, the boundaries of the onions are preserved. Figure 3: A picture before (a) and after (b) bilateral filtering. (c,d) are details from (a,b). 5. Experimental Results: We derive training and test sets for our experiments from natural images in the Berkeley segmentation database, which has been previously used to study denoising [20, 4]. We restrict our experiments to the case of monochrome images; color images in the Berkeley dataset are converted to grayscale by averaging the color channels. The test set consists of 100 images, 77 with dimensions 321_481 and 23 with dimensions 481 _ 321. Quantitative comparisons are performed using the Peak Signal to Noise Ratio (PSNR) and Mean Average Error (MAE) of the output image and comparisons are made based on the Error Image. The Error of the corresponding image is calculated by subtracting the original image from the image we obtained. 5.1. Peak Signal to Noise Ratio Signal-to-noise (SNR) estimates the quality of a reconstructed image compared with the original image. The basic idea is to compute a single number that reflects the quality of the reconstructed image[4]. Reconstructed images with higher metrics are judged better. In fact, traditional SNR measures do not equate with human subjective perception. Several research groups are working on perceptual measures, but for now signal-to-noise measures are used because they are easier to compute. Also to be noted that higher measures do not always mean better quality. The actual metric that is computed in this work is the peak signal-to-reconstructed image measure, which is called PSNR. Assume a source image f(i,j) is given that contains M by N pixels and a reconstructed image F(i,j) where F is reconstructed by decoding the encoded version of f(i,j). Error metrics are computed on the luminance signal only so the pixel values f(i,j) range between black (0) and white (255). First the mean absolute error of the reconstructed image is computed (MAE) as follows (11) The summation is over all pixels. PSNR in decibels (dB)[4] is computed by using PSNR = 10 log 10 (2552 / MAE). (12) 5.2. Error Image The other important technique for displaying errors is to construct an error image which shows the pixel-by-pixel errors. The simplest computation of this image is to create an image by taking the difference between the reconstructed and original pixels. These images are hard to see because zero difference is black and most errors are small numbers which are shades of black. The typical construction of the error image multiples the difference by a constant to increase the visible difference and translates the entire image to a gray level. The computation is E(i,j)=2[f(i,j)-F(i,j)] +128 (13). The constant (2) or the translation (128) can be adjusted to change the image. Some people use white (255) to signify no error and difference from white as an error which means that darker pixels are bigger errors. Figure 4: Denoising results on an image from the test set. The noisy image was generated by adding Gaussian noise with ? = 50 to the clean image. Non-blind Denoising results for the BLS-GSM, FoE, and Convolutional network methods are shown. The lower left panel shows results for the outlined region in the upper left panel. The zoomed in region shows that in some areas CN2 output has less severe artifacts than the wavelet-based results and is sharper than the FoE results. CN1 results (PSNR=24:12) are visually similar to those of CN2. Table below shows the comparison of the three techniques on their quality metrics. S.No. Method PSNR Execution Time 1. Fuzzy Clustering (Fuzzy) 28.24 253.14 2. Fuzzy C Means Clustering (FCM) 30.57 161.71 3. Convolutional Network (CN) 39.39 2.28 6. Conclusion: Out of the three methods tested competitive neural network is found to be good on the basis of image reproduction because of increased PSNR as well as image compression due to the increased compression ratio. We have found that the optimal ?r value of the bilateral filter is linearly related to the standard deviation of the noise. The optimal value of the ?d is relatively independent of the noise power. Based on these results, we estimate the noise variance at each level of the subbands decomposition and use the optimal ?r value for bilateral filtering. The key factor in the performance of the proposed method is the multiresolution application of the bilateral filter. It helped eliminating the coarse-grain noise in images. The wavelet thresholding adds power the proposed method. This work has several applications in various scientific fields like Satellite imaging, Map determination, Medical imaging, Optical character recognition (OCR), Non-Destructive testing, etc. The program developed has been tested with various pictures and the results were proven to be fruitful. The program has also been tested for its consistency and its reliability. References [1] C. Rosenberger,K. Chehdi, â€Å"Unsupervised Clustering Method with Optimal Estimationof the Number of Clusters: Application to Image Segmentation† in the proceedings of 15th International Conference on Pattern Recognition (ICPR00) Volume 1 p. 1656. [2] Sven behnke and nicolaos b. Karayiannis, 1998, â€Å"Competitive neural trees for pattern classification†, in the IEEE transactions on neural networks, vol. 9, no. 6, pp.1352 -1369, november 1998. [3] Rezaee, M.R. van der Zwet, P.M.J. Lelieveldt, B.P.E. van der Geest, R.J. Reiber, J.H.C., 2000, â€Å"A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering† in IEEE Transactions on Image Processing, pp: 1238-1248, Vol. 9, No: 7, Jul 2000. [4] Hung-Ching Lu, Ted Tao,2006, â€Å"Closed-loop method to improve image PSNR in pyramidal CMAC networks† in the Computer Applications in Technology 2006 Vol. 25, No.1 pp. 22 29. [5] R. Krishnapuram, J.M. Keller., 1996, The possibilistic c-means:insights recommendations. IEEE Trans. Fuzzy Systems 4: 385-393, 1996. [6] Songcan Chen, Daoqiang Zhang, 2004, â€Å"Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure† in IEEE Transactions on Systems, Man and Cybernetics, Vol.34,No.4,pp: 1907-1916,Aug 2004. [7] Siddheswar Ray and Rose H Turi, 1999, Determination of number of clusters in k-means clustering and application in colour image segmentation, in 4th International Conference on Advances in Pattern Recognition and Digital Techniques (ICAPRDT99), 1999. [8]. C. Tomasi and R. Manduchi, â€Å"Bilateral filtering for gray and color images,† in Proc. Int. Conf. Computer Vision, 1998, pp.839–846. [9]. S. G. Chang, B. Yu, and M. Vetterli, â€Å"Adaptive wavelet thresholding for image denoising and compression,† Trans. Image Processing, vol. 9, no. 9, pp. 1532–1546, September 2000. [10]. D. L. Donoho and I. M. Johnstone, â€Å"Ideal spatial adaptation by wavelet shrinkage,† Biometrika, vol. 81, no. 3, pp. 425–455, 1994. Research Papers on Image Denoising Based on Soft Computing TechniquesAnalysis Of A Cosmetics AdvertisementIncorporating Risk and Uncertainty Factor in CapitalBionic Assembly System: A New Concept of SelfOpen Architechture a white paperMarketing of Lifeboy Soap A Unilever ProductMind TravelResearch Process Part OneThe Relationship Between Delinquency and Drug UseInfluences of Socio-Economic Status of Married MalesEffects of Television Violence on Children

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