The main field of our research is artificial intelligence already implemented in the experimental autonomous road vehicle. Artificial intelligence now is a really huge area of studies. This investigation is focused more on a development of intelligent systems. Such as expert systems, semantic text processing systems, semantic image understanding systems, robotics systems. Wi!Mi is a solution for developing mivar knowledge models. This tool belongs to the class of so-called ES shells - programs that allow significantly simplify and accelerate the ES development process. It uses a novel knowledge representation mechanism based on bipartite graphs and the significantly improved but simple inference engine, which is able to process large systems involving millions of variables in just a few seconds. A special representation model which is called mivar nets with linear computational complexity using if-then rules is used in Wi!Mi. The idea is that the subject domain is divided into objects and connections between them. Scientific investigation allows to implement new decision-making system called “RoboRazum” as a form of an embedded software platform with flexible capabilities to adapt to control by any robotic suites and systems. RoboRazum is a platform for designing autonomous robotic control systems. Provided solutions don’t require powerful computational resources and could be used in small systems-on-chip like microcontrollers. That is why this solution is perfectly fit with conception of Internet of Things (IoT). Reasoned and fast logical decisions based on mivar technologies play important role for autonomous road vehicles.
This research presents a methodology for creating mivar knowledge bases in tabular-matrix form for ground intelligent vehicle control systems. At its core, this methodology is kind of instruction for analysts facing the task of formalizing knowledge in a given subject area. The result of this formalization is a “knowledge map” created according to a special proposed template. In the future, this template allows forming a knowledge base for a given subject area in the formalism of bipartite oriented mivar networks. As an example, the subject area of ground-based intelligent vehicle control systems is used as a template. The proposed methodology of knowledge formalization makes it possible to simplify the process of creating models in Wi!Mi “Razumator-Consultant” 2.1 and also to level the probability of logical collisions when designing a knowledge model in the formalism of bipartite oriented mivar networks.
Исследованы этапы становления лугопастбищного хозяйства Якутии применительно к табунному коневодству со средних веков до нашего времени. В таёжно-мелкодолинной зоне табунного коневодства Якутии необходимая площадь пастбищ для одной взрослой лошади в бесснежный период (165 дней) составляет 5,2 га при урожайности угодий в среднем по 2 т/га в натуральной массе, в зимний период (200 дней) — 11 га при урожайности угодий 0,53 т/га, или в год — 16–17 га. Создание сеяных травостоев из травосмесей регнерии ленской, костреца безостого, ломколосника ситникового, превосходящих по продуктивности естественные природные травостои в 2–2,5 раза, позволяет повысить конеёмкость кормовых угодий в 2 раза. В таёжно-аласной зоне табунного коневодства Центральной Якутии для создания высокопродуктивных сеяных сенокосно-тебенёвочных угодий на аласных лугах рекомендуются кострец безостый сорта Аммачаан и люцерна сорта Якутская жёлтая, а также кострец в чистом виде. Конеёмкость аласных угодий повышается в 1,5–2 раза. Для использования в качестве замороженных тебенёвочных кормов для молодняка в возрасте до 3 лет и маточного поголовья лошадей якутской породы из однолетних культур высокоэффективны овёс, а также овёс с ячменём в два летних срока посева: 25–30 июня — первый срок, 10–15 июля — второй срок. Пробы зелёной массы овса летнего срока посева в замороженном виде (октябрь-март) по кормовым достоинствам мало отличаются от проб зелёной массы овса, взятых на тех же посевах в тёплое время года. Рекомендуемые сроки тебенёвки молодняка — с середины ноября по апрель; сроки тебенёвки маточного поголовья — с февраля по апрель. При этом для рационального тебенёвочного использования угодий необходимо выделять отдельные участки от 5 до 20–25 га каждый. This review provides insights on pasture cultivation in Yakutia from the Middle Ages to these days. In the taiga-bottomland zone of horse herd farming an adult horse requires 5.2 ha of grazing area under grass productivity of 2 t ha-1, while in winter (200 days) — 11 ha under the productivity of 0.53 t ha-1 (16–17 annually). Planting swards of Regneria spp., smooth brome and Russian wildrye increases forage land productivity by 2 times since these crops exceed natural stands in productivity by 2–2.5 times. To obtain high-productive forage lands in the taiga-alas zone of the Central Yakutia smooth brome “Ammachaan” and alfalfa “Yakutskaya zheltaya” are recommended. Alas productivity increases by 1.5–2 times. Oats or oat mixtures with barley (planted on the 25–30th of June or 10–15th of July) are effective in feeding of young horses (up to 3 years old) or breeding stock as frozen feed. Frozen green mass of oats seeded in summer has similar forage characteristics as the one collected in warm period. The best period for young horse grazing is from the middle of November to April; breeding stock — from February to April. It is necessary to separate areas of 5–25 ha for efficient grazing.
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