Вимірювальна техніка та метрологія. – 2016. – Випуск 77
Permanent URI for this collectionhttps://ena.lpnu.ua/handle/ntb/39773
Міжвідомчий науково-технічний збірник
Вимірювальна техніка та метрологія : міжвідомчий науково-технічний збірник / Міністерство освіти і науки України ; відповідальний редактор Б. І. Стадник. – Львів : Видавництво Львівської політехніки, 2016. – Випуск 77. – 198 c. : іл.
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Item Ітеративна модель управління ризиками приготування небезпечної продукції ресторанного господарства(Видавництво Львівської політехніки, 2016) Сусол, Наталія; Микийчук, Микола; Львівський інститут економіки і туризму, кафедра харчових технологій та ресторанної справи; Національний університет "Львівська політехніка"Розроблено модель управління ризиками небезпечності продукції на стадії виготовлення, що є уніфікованим підходом послідовно інтегрованих ітерацій з комплексом заходів: ідентифікації, оцінювання, моніторингу та контролю ризиків, які ґрунтуються на взаємозв’язку принципів і завдань теорії управління ризиками. Запропонована модель управління є системним підходом постійного визначення ризиків в умовах щоденної виробничої практики підприємств галузі, що є комплексним з набором процедур контролю та моніторингу ризиків. За аналітичним оглядом науково обґрунтованих фактів, доведених результатів лабораторного дослідження, на підставіаналізу їх джерел, можливого впливу на здоров’я та життя споживачів, причин та потенційних наслідків визначено види ризиків небезпечності продукції: ризик харчової токсикоінфекції, ризик токсичного отруєння та ризик невідповідності технічним вимогам. Разработана модель управления рисками опасности продукции на стадии изготовления, является унифицированным подходом последовательно интегрированных итераций с комплексом мероприятий: идентификации, оценки, мониторинга и контроля рисков, основанных на взаимосвязи принципов и задач теории управления рисками. Предложенная модель управления является системным подходом постоянного определения рисков в условиях ежедневной производственной практики предприятий отрасли, имеет комплексный характер с набором мер контроля и мониторинга рисков. Согласно аналитическому обзору научно обоснованных фактов, доказанных результатов лабораторного исследования, исходя из анализа их источников, возможного влияния на здоровье ижизнь потребителей, причин и потенциальных последствий определены виды рисков опасности продукции: риск пищевой токсикоинфекции, риск токсического отравления и риск несоответствия техническим требованиям. The model of hazard risk management of products at the manufacturing stage is designed, which is a unified approach of the consistently integrated iterations with a set of measures of identification, assessment, monitoring and risk control based on the correlation of the principles and objectives of risk management. The offered model of management is a systematic approach to identify the risks in terms of daily production practices of the industry enterprises that have comprehensive nature with the set of controls and risks monitoring. By the analytical review of scientifically based facts proven results of laboratory research, based on the analysis of their sources, possible effect on health and lives of consumers, causes and potential consequences, the types of hazard risk of products are defined: the risk of food poisoning, the risk of toxic poisoning and the risk of the technical requirements inconsistency. The branch specificity of the manufacturing a dangerous product risks is defined which is expressed in multi-spectrality multiplacativity of the threats that complicate risk hazards complementing each other. The effect of multiplacativity of risks as a result of each other complementation or various risks blending, their dispersion in time and space, can significantly complicate the effects of risks. The research results underline that the responsibility for risk emergence during manufacture and sale of dangerous products is up to the producers. The risk of dangerous sources and factors is different. However, the most frequent causes of increased risks of manufacturing dangerous products are as follows: the violation of cooking technology and culinary products (ways and processing methods, thermal regimes, etc.) improper manufacturing and hygiene practices; cross-contamination due to violation of production facilities; violation of production sales requirements. The most typical risks of dangerous products manufacturing in the enterprises of restaurant business are considered, depending on the origin, and are grouped into three main groups of risks: · resource (poor quality of raw materials, inadequate storage conditions of goods in stock); · production, technical and technological (manufacturing and hygienic practices (Good Manufacturing Practice – GMP, Good hygiene practice – GHP), appropriate methods of technological processing of raw materials in cooking); · improper maintenance and sale of finished products. Defining the indicators of danger of the restaurant industry products was made by major types of raw materials, by the mass fraction, by the acceptable levels of normalized indicators. The hazard of products of the restaurant industry by the level of the hazard severity can be described by the following risks: § the risk of food poisoning (caused by microbes, viruses or protozoa or their metabolic products); § the risk of toxic poisoning (after chemical toxins, heavy metals, toxins, pesticides, nitrates, nitrites, food additives get into the body through food, the use of herbs, plants or inedible mushrooms); § the risk of the technical requirements inconsistency (discrepancy of energy value, organoleptic characteristics, conditions and methods of sales, etc.). Designed iterative model of risk management involves continuous determination of possible risks covering all stages of production and sales which allows minimizing or preventing the emergence of danger.Item Розумні вимірювальні засоби для кіберфізичних систем(Видавництво Львівської політехніки, 2016) Микийчук, Микола; Стадник, Богдан; Яцишин, Святослав; Луцик, Ярослав; Національний університет “Львівська політехніка”Праця спрямована на розвиток кіберфізичних систем, які стають ключовим фактором повсякденного життя, а розумні вимірювальні прилади вважають невід’ємним компонентом цієї системи. Розглядається верифікація метрологічних підсистем за параметрами, що визначають керованість обладнання та процесів, розробленням, впровадженням та реалізацією конкретних метрологічних методів та інструментів, які успішно описуються термінами “апаратна підтримка, основне і проміжне метрологічне програмне забезпечення”. Работа направлена на развитие киберфизических систем, которые становятся ключевым фактором повседневной жизни, а умные измерительные приборы считаются неотъемлемым компонентом этой системы. Рассматривается верификация метрологических подсистем по параметрам, определяющим управляемость оборудования и процессов, путем разработки, внедрения и реализации конкретных метрологических методов и инструментов, которые успешно описываются терминами “аппаратная поддержка, основное и промежуточное метрологическое программное обеспечение”. Smart measuring instruments are the prerequisite for CPS design as they constitute the essential units of informationmeasuring subsystems. There is a set of smart measurement instruments which is divided into the following subsets: smart sensors, smart transducers, their grids etc. that can be joined together in modern wireless sensor networks. The emerging field of cheap and easily deployed sensors offers an unprecedented opportunity for a wide spectrum of various applications. When combined, they offer numerous advantages over traditional networks. These include a large-scale flexible architecture, high-resolution data, and application-adaptive mechanisms as well as a row of metrological specific features and performance (self-check, self-validation, self-verification, self-calibration, self-adjustment). Milestones in everyday work aiming to ensure reliable wireless sensors networks operation lie in the direction of functional and probabilistic verifications. We provide the software and middleware development aiming to reach predetermined behavior. The easiest way to achieve this may be demonstrated on the example of widespread wireless fire detector networks. They are characterized by a number of special algorithms directed on as fast as possible and accurate triggering and actuating the automation of higher level. So, it becomes necessary to research and implement the original operation algorithms for fire sensors and also check algorithms for periodic real-time software examination. Considering their structural complexity (presence of smoke and heat sensitive elements, various principles of elaboration of the received signals, their drift of characteristics, and pollution of translucent elements, etc.) the development of such algorithms is a daunting task. Herein, human life may be the price for a bug. Equally important seems to be probabilistic verification that is to boost the probability of reaching wireless sensors network declared goals (estimation of their chances being achieved). Each network consists structurally of a large number (up to 103) of nodes which are individual sensors able to radio communicate with one or several neighboring units. The most common wireless sensors network is the fire alarm sensors network each branch of which has up to 26 sensors which was caused by limiting the length of microcontroller register. Topology of every network may differ: star, cluster tree, mesh, up to advanced multi-hop mesh network. Propagation technique between hops of network can be routing or flooding. Nowadays, problem arises to adapt traditional network topologies to contemporary communicating conditions. If a centralized architecture is used in a sensor network and the central node fails, then the entire network will collapse, however the reliability of sensor network can be increased by using distributed control architecture. Distributed control is used in such networks for the following reasons: sensor nodes are prone to failure; for better collection of data; to provide nodes with backup in case of the central node failure; resources have to be self -organized. Aiming at the substantial development of Cyber-Physical systems, which are becoming a key element of everyday life,the smart measuring instruments are considered below as an indispensable part of entire systems. Verification of the metrological subsystems for parameters determining the controlled equipment and processes through the development,implementation and realization of specific metrology and standardization methods, instruments, that is successfully described by the terms “metrological hardware, software, and middleware”. Smart sensors are supplied with digital information transmissive means by equipping them with built-in digital controllers to match the universal network interface or by combining technology of analogue and digital transmission in a single measuring channel. According to the structure all smart sensors are divided into 4 groups: sensors of centralized and decentralized types, as well as sensors with digital and analogue buses. According to correction methods the analogue interfaces with smart sensors are divided into the groups: with manual error correction, with auto correction of errors in analogue-digital form, and with digital correction of errors. Specific measurement consists in evaluating MIs performance reliability, trueness, and other metrological properties, due to the quality of a certain kind of metrological software, or the software linked to metrological features of MIs. MI software metrological verification raises the problem of appropriate methods choice of software and middleware assessing, testing, and certifying. The metrological validation must result in confirmation or discarding of the studied ware following the requirements indicated in normative documents. Procedures and methods of checking software, and determining its disadvantages are considered below. Software study includes first of all the fulfilling the procedures of inambiguity ensuring the operating functions for generated data. Selection of the procedures is determined by regulation requirements, as well as by the software developer or the user’s desires to confirm its compliance with the target specification.