Localization Industry Standards Association or LISA was a Swiss-based trade body concerning the translation of computer software (and associated materials) into multiple natural languages, which existed from 1990 to February 2011. It counted among its members most of the large information technology companies of the period, including Adobe, Cisco, Hewlett-Packard, IBM, McAfee, Nokia, Novell and Xerox. LISA played a significant role in representing its partners at the International Organization for Standardization (ISO), and the TermBase eXchange (TBX) standard developed by LISA was submitted to ISO in 2007 and became ISO 30042:2008. LISA also had a presence at the W3C. A number of the LISA standards are used by the OASIS Open Architecture for XML Authoring and Localization framework. LISA shut down on 28 February 2011, and its website went offline shortly afterwards. In the wake of the closure of LISA, the European Telecommunications Standards Institute started an Industry Specification Group (ISG) for localization. The ISG has five work items: Term-Base eXchange (TBX) / ISO 30042:2008 Translation Memory eXchange (TMX), with GALA Segmentation Rules eXchange (SRX) / ISO/CD 24621) Global information management Metrics eXchange – Volume (GMX-V); Another organization that was formed in response to the closure of LISA is Terminology for Large Organizations (TerminOrgs), a consortium of terminology professionals who promote terminology management best practices.
Computer appliance
A computer appliance is a computer system with a combination of hardware, software, or firmware that is specifically designed to provide a particular computing resource. Such devices became known as appliances because of the similarity in role or management to a home appliance, which are generally closed and sealed, and are not serviceable by the user or owner. The hardware and software are delivered as an integrated product and may even be pre-configured before delivery to a customer, to provide a turn-key solution for a particular application. Unlike general purpose computers, appliances are generally not designed to allow the customers to change the software and the underlying operating system, or to flexibly reconfigure the hardware. Another form of appliance is the virtual appliance, which has similar functionality to a dedicated hardware appliance, but is distributed as a software virtual machine image for a hypervisor-equipped device. == Overview == Traditionally, software applications run on top of a general-purpose operating system, which uses the hardware resources of the computer (primarily memory, disk storage, processing power, and networking bandwidth) to meet the computing needs of the user. The main issue with the traditional model is related to complexity. It is complex to integrate the operating system and applications with a hardware platform, and complex to support it afterwards. By tightly constraining the variations of the hardware and software, the appliance becomes easily deployable, and can be used without nearly as wide (or deep) IT knowledge. Additionally, when problems and errors appear, the supporting staff very rarely needs to explore them deeply to understand the matter thoroughly. The staff needs merely training on the appliance management software to be able to resolve most of problems. In all forms of the computer appliance model, customers benefit from easy operations. The appliance has exactly one combination of hardware and operating system and application software, which has been pre-installed at the factory. This prevents customers from needing to perform complex integration work, and dramatically simplifies troubleshooting. In fact, this "turnkey operation" characteristic is the driving benefit that customers seek when purchasing appliances. To be considered an appliance, the (hardware) device needs to be integrated with software, and both are supplied as a package. This distinguishes appliances from "home grown" solutions, or solutions requiring complex implementations by integrators or value-added resellers (VARs). The appliance approach helps to decouple the various systems and applications, for example in the data center. Once a resource is decoupled, in theory it can be also centralized to become shared among many systems, centrally managed and optimized, all without requiring changes to any other system. == Tradeoffs of the computer appliance approach == The major disadvantage of deploying a computer appliance is that since they are designed to supply a specific resource, they most often include a customized operating system running over specialized hardware, neither of which are likely to be compatible with the other systems previously deployed. Customers lose flexibility. One may believe that a proprietary embedded operating system, or operating system within an application, can make the appliance much more secure from common cyber attacks. However, the opposite is true. Security by obscurity is a poor security decision, and appliances are often plagued by security issues as evidenced by the proliferation of IoT devices. == Types of appliances == The variety of computer appliances reflects the wide range of computing resources they provide to applications. Some examples: Storage appliances provide large amounts of storage, often available to many machines on the network. See Network-attached storage and Storage area network. Network appliances are general purpose routers which may also provide firewall protection, Transport Layer Security (TLS), messaging, access to specialized networking protocols (like the ebXML Message Service) and bandwidth multiplexing for the multiple systems they front-end. Backup and disaster recovery appliances computer appliances that are integrated backup software and backup targets, sometimes with hypervisors to support local DR of protected servers. They are often a gateway to a full DRaaS solution. Firewall and Security appliances Dedicated network appliances that are designed to protect computer networks from unwanted traffic. IIoT and MES Gateway appliances Computer appliances that are designed to translate data bidirectionally between control systems and enterprise systems. Proprietary, embedded, firmware applications running on the appliance use point-to-point connections to translate data between field devices in their native automation protocols and MES systems through their APIs, ODBC, or RESTful interfaces. Anti-spam appliances for e-mail spam Software appliances A single application server appliance, with just enough operating system (JeOS) for it to run. Virtual machine appliances consist of a "hypervisor style" embedded operating system running on appliance hardware. The hypervisor layer is matched to the hardware of the appliance, and cannot be varied by the customer, but the customer may load other operating systems and applications onto the appliance in the form of virtual machines. == Consumer appliances == Aside from its deployment within data centers, many computer appliances are directly used by the general public. These include: Digital video recorder Residential gateway Network-attached storage (NAS) Video game console Consumer uses stress the need for an appliance to have easy installation, configuration, and operation, with little or no technical knowledge being necessary. == Appliances in industrial automation == The world of industrial automation has been rich in appliances. These appliances have been hardened to withstand temperature and vibration extremes. These appliances are also highly configurable, enabling customization to meet a wide variety of applications. The key benefits of an appliance in automation are: Reduced downtime - a failed appliance is typically replaced with a COTS replacement and its task is quickly and easily reloaded from a backup. Highly scalable - appliances are typically targeted solutions for an area of a plant or process. As the requirements change, scalability is achieved through the installation of another appliance. Automation concepts are easily replicated throughout the enterprise by standardizing on appliances to perform the needed tasks, as opposed to the development of custom automation schemes for each task. Low TCO (total cost of ownership) - appliances are developed, tested and supported by automation product vendors and undergo a much broader level of quality testing than custom designed automation solutions. The use of appliances in automation reduce the level of testing needed in each individual application. Reduced design time - appliances perform specific functions and although they are highly configurable, they are typically self documenting. This enables appliance based solutions to be transferred from engineer to engineer with minimal need for training and documentation. Types of automation appliances: PLC (programmable logic controller) - Programmable logic controllers are appliances that are typically used for discrete control and offer a wide range of Input and Output options. They are configured through standardized programming languages such as IEC-1131. PID (proportional–integral–derivative controller) - PID controllers are appliances that monitor a process variable and, based on an error term, effect change on a control output (manipulated variable) to drive the process variable to a setpoint. PAC (programmable automation controller) - Programmable automation controllers are appliances that embody properties of both PLCs and PID controllers enabling the integration of both analog and discrete control. Universal gateway - A universal gateway appliance has the ability to communicate with a variety of devices through their respective communication protocols, and will affect data transactions between them. This in increasingly important as manufacturing strives to improve agility, quality, production rates, production costs and reduce downtime through enhanced M2M (machine to machine) communications. EATMs (Enterprise Appliance Transaction Modules) - Enterprise appliance transaction modules are appliances that affect data transactions from plant floor automation systems to enterprise business systems. They communicate to plant floor equipment through various vendor automation protocols, and communicate to business systems through database communication protocols such as JMS (Java Message Service) and SQL (Structured Query Language). == Internal structure == There are several
NYSERNet
NYSERNet, Inc. (New York State Education and Research Network), is a non-profit Internet service provider in New York State. It mainly provides Internet access to universities, colleges, museums, health care facilities, primary and secondary schools, and research institutions. == History == NYSERNet was founded in 1986 in Troy, New York. Its founders compared NYSERNet's network with the Erie Canal and considered it the next step in two centuries to draw the country together. NYSERNet's network reaches from Buffalo to New York City. Completed in 1987, it was the first statewide regional IP network in the United States.[1] Initial speed of 56 kbps was upgraded to T1 in 1989 and T3 in 1994. It was the original assignee of AS174 according to RFC1117. This ASN is used today by Cogent Communications for their global network.
Data validation and reconciliation
Industrial process data validation and reconciliation, or more briefly, process data reconciliation (PDR), is a technology that uses process information and mathematical methods in order to automatically ensure data validation and reconciliation by correcting measurements in industrial processes. The use of PDR allows for extracting accurate and reliable information about the state of industry processes from raw measurement data and produces a single consistent set of data representing the most likely process operation. == Models, data and measurement errors == Industrial processes, for example chemical or thermodynamic processes in chemical plants, refineries, oil or gas production sites, or power plants, are often represented by two fundamental means: Models that express the general structure of the processes, Data that reflects the state of the processes at a given point in time. Models can have different levels of detail, for example one can incorporate simple mass or compound conservation balances, or more advanced thermodynamic models including energy conservation laws. Mathematically the model can be expressed by a nonlinear system of equations F ( y ) = 0 {\displaystyle F(y)=0\,} in the variables y = ( y 1 , … , y n ) {\displaystyle y=(y_{1},\ldots ,y_{n})} , which incorporates all the above-mentioned system constraints (for example the mass or heat balances around a unit). A variable could be the temperature or the pressure at a certain place in the plant. === Error types === Data originates typically from measurements taken at different places throughout the industrial site, for example temperature, pressure, volumetric flow rate measurements etc. To understand the basic principles of PDR, it is important to first recognize that plant measurements are never 100% correct, i.e. raw measurement y {\displaystyle y\,} is not a solution of the nonlinear system F ( y ) = 0 {\displaystyle F(y)=0\,\!} . When using measurements without correction to generate plant balances, it is common to have incoherencies. Measurement errors can be categorized into two basic types: random errors due to intrinsic sensor accuracy and systematic errors (or gross errors) due to sensor calibration or faulty data transmission. Random errors means that the measurement y {\displaystyle y\,\!} is a random variable with mean y ∗ {\displaystyle y^{}\,\!} , where y ∗ {\displaystyle y^{}\,\!} is the true value that is typically not known. A systematic error on the other hand is characterized by a measurement y {\displaystyle y\,\!} which is a random variable with mean y ¯ {\displaystyle {\bar {y}}\,\!} , which is not equal to the true value y ∗ {\displaystyle y^{}\,} . For ease in deriving and implementing an optimal estimation solution, and based on arguments that errors are the sum of many factors (so that the Central limit theorem has some effect), data reconciliation assumes these errors are normally distributed. Other sources of errors when calculating plant balances include process faults such as leaks, unmodeled heat losses, incorrect physical properties or other physical parameters used in equations, and incorrect structure such as unmodeled bypass lines. Other errors include unmodeled plant dynamics such as holdup changes, and other instabilities in plant operations that violate steady state (algebraic) models. Additional dynamic errors arise when measurements and samples are not taken at the same time, especially lab analyses. The normal practice of using time averages for the data input partly reduces the dynamic problems. However, that does not completely resolve timing inconsistencies for infrequently-sampled data like lab analyses. This use of average values, like a moving average, acts as a low-pass filter, so high frequency noise is mostly eliminated. The result is that, in practice, data reconciliation is mainly making adjustments to correct systematic errors like biases. === Necessity of removing measurement errors === ISA-95 is the international standard for the integration of enterprise and control systems It asserts that: Data reconciliation is a serious issue for enterprise-control integration. The data have to be valid to be useful for the enterprise system. The data must often be determined from physical measurements that have associated error factors. This must usually be converted into exact values for the enterprise system. This conversion may require manual, or intelligent reconciliation of the converted values [...]. Systems must be set up to ensure that accurate data are sent to production and from production. Inadvertent operator or clerical errors may result in too much production, too little production, the wrong production, incorrect inventory, or missing inventory. == History == PDR has become more and more important due to industrial processes that are becoming more and more complex. PDR started in the early 1960s with applications aiming at closing material balances in production processes where raw measurements were available for all variables. At the same time the problem of gross error identification and elimination has been presented. In the late 1960s and 1970s unmeasured variables were taken into account in the data reconciliation process., PDR also became more mature by considering general nonlinear equation systems coming from thermodynamic models., , Quasi steady state dynamics for filtering and simultaneous parameter estimation over time were introduced in 1977 by Stanley and Mah. Dynamic PDR was formulated as a nonlinear optimization problem by Liebman et al. in 1992. == Data reconciliation == Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors. From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation. Given n {\displaystyle n} measurements y i {\displaystyle y_{i}} , data reconciliation can mathematically be expressed as an optimization problem of the following form: min x , y ∗ ∑ i = 1 n ( y i ∗ − y i σ i ) 2 subject to F ( x , y ∗ ) = 0 y min ≤ y ∗ ≤ y max x min ≤ x ≤ x max , {\displaystyle {\begin{aligned}\min _{x,y^{}}&\sum _{i=1}^{n}\left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}\\{\text{subject to }}&F(x,y^{})=0\\&y_{\min }\leq y^{}\leq y_{\max }\\&x_{\min }\leq x\leq x_{\max },\end{aligned}}\,\!} where y i ∗ {\displaystyle y_{i}^{}\,\!} is the reconciled value of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), y i {\displaystyle y_{i}\,\!} is the measured value of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), x j {\displaystyle x_{j}\,\!} is the j {\displaystyle j} -th unmeasured variable ( j = 1 , … , m {\displaystyle j=1,\ldots ,m\,\!} ), and σ i {\displaystyle \sigma _{i}\,\!} is the standard deviation of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), F ( x , y ∗ ) = 0 {\displaystyle F(x,y^{})=0\,\!} are the p {\displaystyle p\,\!} process equality constraints and x min , x max , y min , y max {\displaystyle x_{\min },x_{\max },y_{\min },y_{\max }\,\!} are the bounds on the measured and unmeasured variables. The term ( y i ∗ − y i σ i ) 2 {\displaystyle \left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}\,\!} is called the penalty of measurement i. The objective function is the sum of the penalties, which will be denoted in the following by f ( y ∗ ) = ∑ i = 1 n ( y i ∗ − y i σ i ) 2 {\displaystyle f(y^{})=\sum _{i=1}^{n}\left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}} . In other words, one wants to minimize the overall correction (measured in the least squares term) that is needed in order to satisfy the system constraints. Additionally, each least squares term is weighted by the standard deviation of the corresponding measurement. The standard deviation is related to the accuracy of the measurement. For example, at a 95% confidence level, the standard deviation is about half the accuracy. === Redundancy === Data reconciliation relies strongly on the concept of redundancy to correct the measurements as little as possible in order to satisfy the process constraints. Here, redundancy is defined differently from redundancy in information theory. Instead, redundancy arises from combining sensor data with the model (algebraic constraints), sometimes more specifically called "spatial redundancy", "analytical redundancy", or "topological redundancy". Redundancy can be due to sensor redundancy, where sensors are duplicated in order to have more than one measurement of the same quantity. Redundancy also arises when a single variable can be estimated in several independent ways from separate sets of measurements at a given time or time averaging period, using the algebraic constraints. Redundancy is linked to the concept
NYSERNet
NYSERNet, Inc. (New York State Education and Research Network), is a non-profit Internet service provider in New York State. It mainly provides Internet access to universities, colleges, museums, health care facilities, primary and secondary schools, and research institutions. == History == NYSERNet was founded in 1986 in Troy, New York. Its founders compared NYSERNet's network with the Erie Canal and considered it the next step in two centuries to draw the country together. NYSERNet's network reaches from Buffalo to New York City. Completed in 1987, it was the first statewide regional IP network in the United States.[1] Initial speed of 56 kbps was upgraded to T1 in 1989 and T3 in 1994. It was the original assignee of AS174 according to RFC1117. This ASN is used today by Cogent Communications for their global network.
PCVC Speech Dataset
The PCVC (Persian Consonant Vowel Combination) Speech Dataset is a Modern Persian speech corpus for speech recognition and also speaker recognition. The dataset contains sound samples of Modern Persian combination of vowel and consonant phonemes from different speakers. Every sound sample contains just one consonant and one vowel So it is somehow labeled in phoneme level. This dataset consists of 23 Persian consonants and 6 vowels. The sound samples are all possible combinations of vowels and consonants (138 samples for each speaker). The sample rate of all speech samples is 48000 which means there are 48000 sound samples in every 1 second. Every sound sample starts with consonant then continues with vowel. In each sample, in average, 0.5 second of each sample is speech and the rest is silence. Each sound sample ends with silence. All of sound samples are denoised with "Adaptive noise reduction" algorithm. Compared to Farsdat speech dataset and Persian speech corpus it is more easy to use because it is prepared in .mat data files. Also it is more based on phoneme based separation and all samples are denoised. == Contents == The corpus is downloadable from its Kaggle web page, and contains the following: .mat data files of sound samples in a 23630000 matrix, in which 23 is number of consonants, 6 is the number of vowels and 30000 is the length of sound sample.
Control-flow diagram
A control-flow diagram (CFD) is a diagram to describe the control flow of a business process, process or review. Control-flow diagrams were developed in the 1950s, and are widely used in multiple engineering disciplines. They are one of the classic business process modeling methodologies, along with flow charts, drakon-charts, data flow diagrams, functional flow block diagram, Gantt charts, PERT diagrams, and IDEF. == Overview == A control-flow diagram can consist of a subdivision to show sequential steps, with if-then-else conditions, repetition, and/or case conditions. Suitably annotated geometrical figures are used to represent operations, data, or equipment, and arrows are used to indicate the sequential flow from one to another. There are several types of control-flow diagrams, for example: Change-control-flow diagram, used in project management Configuration-decision control-flow diagram, used in configuration management Process-control-flow diagram, used in process management Quality-control-flow diagram, used in quality control. In software and systems development, control-flow diagrams can be used in control-flow analysis, data-flow analysis, algorithm analysis, and simulation. Control and data are most applicable for real time and data-driven systems. These flow analyses transform logic and data requirements text into graphic flows which are easier to analyze than the text. PERT, state transition, and transaction diagrams are examples of control-flow diagrams. == Types of control-flow diagrams == === Process-control-flow diagram === A flow diagram can be developed for the process [control system] for each critical activity. Process control is normally a closed cycle in which a sensor. The application determines if the sensor information is within the predetermined (or calculated) data parameters and constraints. The results of this comparison, which controls the critical component. This [feedback] may control the component electronically or may indicate the need for a manual action. This closed-cycle process has many checks and balances to ensure that it stays safe. It may be fully computer controlled and automated, or it may be a hybrid in which only the sensor is automated and the action requires manual intervention. Further, some process control systems may use prior generations of hardware and software, while others are state of the art. === Performance-seeking control-flow diagram === The figure presents an example of a performance-seeking control-flow diagram of the algorithm. The control law consists of estimation, modeling, and optimization processes. In the Kalman filter estimator, the inputs, outputs, and residuals were recorded. At the compact propulsion-system-modeling stage, all the estimated inlet and engine parameters were recorded. In addition to temperatures, pressures, and control positions, such estimated parameters as stall margins, thrust, and drag components were recorded. In the optimization phase, the operating-condition constraints, optimal solution, and linear-programming health-status condition codes were recorded. Finally, the actual commands that were sent to the engine through the DEEC were recorded.