114 Smart Information Gathering Support of Mechatronic System Orest Ivakhiv, Markian Nakonechnyi, Oleksandr Viter, Radyslav Behota Computer Technology, Automation and Metrology Institute, Lviv Polytechnic National University, UKRAINE, Lviv, 12 Bandera str., email: oresti@polynet.lviv.ua Abstract: The smart programmable systems often are properties, which are corresponded with the form of very convenient for the measurement object semantic correlation function or its parameters values. These properties reducing. The object state analyzing unit is the core of the are discovered by every the i-th source activity manifestation. system. There is considered that an object behavior a A proper regular type system-sampling program depends on priory is unknown. Corresponding to this mode it is this set of a priory known or estimated activities. If the object necessary to observe an object state and prepare a proper state a priory is poor known, it needs adaptation to the current survey program. The main characteristics of proposed object situation. It is typical remote investigated objects, i.e. system are investigated in this paper. the deep space invention instrumentation or dirty territory Keywords: smart, programmable, flexible, adaptive, serving. In this case, the intelligent multiplex system activity, compression, system. implantation is considered as well operating and the intelligent measurement instrumentation functions should be I. INTRODUCTION extended by the implantation of the task of the observed A mechatronic system involves a set of sensors, object state identification, the inspected parameters real conditioners of sensors’ signals, processing and decision- activities to the adopted sampling program adequacy learning making unit and actuator [1]. Combination system software and in this sense the external situation registration [9]. and hardware with Internet as a communication link give us a cyber-physical system [2]. Obviously, information-gathering II. REGULAR TYPE SYSTEM STRUCTURE support is realized as multiplex time division systems. They The structure of the smart multi-channel tool (Fig. 1) are widely shared in the different areas of human activity includes a unit of a measurement object totality sources such as scientific research, astronautics, agriculture, space behavior analyzing BAU [10], an object state observing unit investigation, image processing and data monitoring systems SOU, a unit of survey programs storage PSU, a unit CMU for health care, which simultaneously monitors, transmits by combining codec/modem functions, i.e., an analog-to-digital radio a records data relating to a plurality of physiological conversion, a noise immunity encoding and modulation, parameters etc. [3-6]. Apart from the demands for small size, which is connected to the communication link (point1). lightweight and long operational lifetime, the sensor systems 2 3 should preferably also be flexible, versatile and intelligent [6]. The traditional approach of reconstructing signals or images from measured data follows the well-known Shannon sampling theorem, which states that the sampling 7 rate must be twice the highest frequency. It was found that at any given time, only a fraction of the neurons were active therefore it was possible to reduce data by 97%. Therefore, it is necessary to use some compression techniques possibility. BAU 1 The aim of data compression is to reduce redundancy in SOU CMU stored or communicated data, thus increasing effective data density. Data transmission, compression and decompression of data files for storage is essentially the same task as sending 8 and receiving compressed data over a communication channel. Compressive sensing is a new type of sampling 4 5 theory, which predicts that sparse signals and images can be 6 reconstructed from what was previously believed to be PSU incomplete information [7,8]. 9 Sensors set which are checking behaviors of object Fig.1. The structure of the multi-channel tool parameters reflect an object state. Sampling interval of a sensor signal is corresponded with both signal frequency Outputs of analog signal sources ( i = 1, n ) simultaneously properties and desired error of analog signal renovation at the are connected to the corresponding inputs of a unit BAU receiver side. It was taken partly stationary zero-mean (points 2 and 3) and an observing unit SOU. A unit PSU sets random process as every signal mathematical model. Any for the unit SOU the sequence of source sampling procedure stationary interval differs from another by the frequency (points 4 and 5). A unit SOU observes the accumulation of ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic 115 absolute sampling errors from the sources totality and after sampled value at the each sampling tact. On the next step, the analyzing procedure, if it is necessary informs the program subtrahend will be this new value, and not the first sample storage unit PSU about the need to change the survey value of the i-th source program using the signal at the point 6. Simultaneously, a Let us consider that sensor analog signals are well unit BAU analyzes analog sources current activities. described by mathematical model as zero-mean partly stationary random process. So, let us take some realization II. OBJECT BEHAVIOR STATE ESTIMATION UNIT ui (t ) of the i-th random signal (Fig.3) which is regular For characteristics setting of this unit a compliance of sampled in moments t j and t j +1 with a sampling interval survey software to the current situation can be accessed through tracking amount behavior of the sampling errors equal to T0 = t j +1 − t j . In this case, one can obtain an absolute from all totality sources during a cycle of the survey. As well and its relative value is as follow as there are added the random variables therefore relevant point and interval statistic estimates (thresholds) can be found (ω1i ⋅ σ i ⋅ Toi ) or δ si2 = ∆ u2si = 1 (ω1i ⋅ Toi )2 , 2 1 for their sums. No exceeding of this threshold with a given ∆ u si2 = (1) confidence probability is identical to matching the real 3 σi 3 current situation at the measurement object, and its exceeding it is the message about the current situation change and here ω1i and σ i are mean-square frequency and mean-square demands on the need of transition to a more relevant survey deviation of the i-th signal, and thus, Toi is its sampling program. Comparison of the threshold value and the sum of ∞ ∞ sampling errors is carried out at every step of the survey. interval ( ω12i = ω 2 G (ω )dω In this unit (Fig.2) outputs of analog sources are ∫ ∫ G(ω )dω , here G(ω) is connected to the proper inputs (points 2 and 3) of unit SOU. 0 0 2 3 power spectral density). ui(t) Toi SOU τ 7 ME11 SE11 Sw21 Voltage TV Sw11 t 0 CE AU tj t tj+1 8 Time ME1n SE1n Sw2n Fig.3. Sampling error vs time dependence ( τ = t − t j ) Sw1n If a sampling program coincides with an object current state then a sum of sampling error from all sources totality (at 4 5 6 the output of adder unit AU) do not exceed the settled 9 PSU threshold value (of the output of unit TV). But when an object state changes due to its new environment situation then Fig.2.The structure of object state observing unit SOU used sampling program becomes wrong, everyone source At the very beginning (at the first step) the sample values sampling error and its sum also become differ from supposed from each source ( i = 1, n ) are recorded in its memory for same partly stationary interval. It demands to change sampling program and therefore corresponding signal appears element ME 1i through the open switch Sw 2i . At the second at the output of comparative element CE (point 6). and subsequent steps the sample value of each source enters Compliance of survey program to the current situation is to the subtraction element SE 1i and passes through open determined according to mean-square deviation of switch Sw 2i and the Codec/Modem unit CMU to the device summarized sampling error from all sources together. In output, i.e., to the communication link (point 1) accordance with the law of large numbers, it can be assumed corresponding with a proper sampling program. The switch that the total error as a random value will be well described Sw 2i is open by the control signal from programmer storage by Student's or Gauss's distribution law. Therefore, with some PSU (points 4 and 5), if it is provided by the current sampling (survey) program. Meantime this signal closes the switch credential probability Ptol one can set the guarantee interval Sw 1i for the same i-th channel. Therefore, the difference from for the total sampling error the i-th subtraction element SE 1i is disconnected from an adder unit AU, i.e., it does not take part in totality sources n sum formation. In the element SE 1i the value recorded in the ME 1i at the previous step is subtracted from the current xtol = ± tα ⋅ σ sΣ or xtol = ± tα ⋅ δsΣ , and δ sΣ = ∑δ , (2) i =1 2 si ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic 116 here, tα is the guarantee coefficient of the set credential 9 2 3 probability. The resulting error from the output of AU is compared with the calculated by unit SOU threshold value (2) from TV element. Signal of excess is fed into the PSU unit (point 9). EU SW31 ME21 SE21 Since the sampling error is sign-alternating, its average value is equial to zero and the variance coincides with the second raw moment. Therefore if errors of all channels of a D1 multi-channel device are independent, total error variance is estimated as follows ∑ ∆u = 3 (ω ⋅ σ ⋅ T ) or δ = 3 ∑ (ω ⋅ T ) (3) 1 1 σ s2Σ = 2 si 1i i oi 2 2 sΣ 1i oi 2 i= i= AEE SW3n ME2n SE2n Note that since the power of the i-th measurement signal is ∞ Dn ∫ G (ω )dω 1 described by expression σ i2 = and 2π i 0 generalized spectral power density GΣ (ω ) = ∑ G (ω ) , certain i i n 1 BAU individual signals being independent, the following equality Fig.4. Behavior analyzing unit structure will be true: σ Σ2 = ∑ i σ i2 In accordance with the principle of adaptive switchboard operation [11], the intensity of the i-th source λi is inverted III. ANALYZING UNIT CHARACTERISTICS ESTIMATION to the average interval τ between two serial activity manifestations of the same i-th source. Let us consider that Here also was provided to analyze the object state by all any current error ∆ (t ) is described by random set of triangles totality sources activities observation. This procedure is (fig.6). realized by analyzing unit BAU (Fig.4). For example, it can be based on the adaptive switchboard principle using [11], ∆i , ∆max τi ∆max i.e., at the every analyzing step it is chosen the most active among totality sources (fig.5). Each sensor activity measure is taken as the i-th signal current deviation from its previous sampled value until analyzed moment. This deviation is ∆і normalized after its mean-square deviation value. Larger normalized deviation is equivalent to more active source. Each i-th difference value is prepared by subtraction of the t 0 sample stored in memory element ME 2i from its current sample from the sources outputs (points 2, 3). They are fixed T ∆ eq.max by the i-th subtraction element SE 2i then passed through divider Di to the activity estimation element AEE. Fig.5. Formation of modulus of sampling errors maxima This element notes the k-th most active source as well as puts a control signal at its corresponding output. It allows ∆ rewriting the value of the most active source in analyzed moment in the k-th memory element ME 2k through an open switch Sw 3k . This allowance is realized by opening a corresponding switch Sw 3k . It is a preparation to the next Z activity analyzing procedure. The number of each source αz t activities manifestations during analyzing interval Tα is fixed 0 τ in element AEE for the proper sampling program at this Fig.6. Dependence of sampling error in time partly stationary interval formation. The sampling program of the current partly stationary object state is checked by Interval τ is defined as τ = z tgαz . Since the random estimation unit EU and passed to unit PSU (point 9). variables z and tgαz are mutually independent, the approximate record looks like the following: ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic 117 τ = z tgα z (4) German Mechatronics Association, 2017 - DOI: The value of the average tangent of angle α z is equal to an 10.1109/REM.2017.8075250 . [2] Industry 4.0: the fourth industrial revolution – guide to average value of source process derivative modulus as follow Industrie 4.0 - https://www.i-scoop.eu/industry-4-0/ • (tgα z ) = ξ . There is known expression for a normal law of [3] Georg E. Fanter, Paul Hegarty, Johannes H. Kindt, and Georg Schitter, “Data acquisition system for high speed atomic force microscopy”, Review of Scientific • Instruments, 76, 026118, 2005. 2 2 the process distribution, i.e., ξ = σ • = ω1i ⋅ σ i , where [4] Johannes Gutleber, Steven Murray, Luciano Orsini, ξ π π “Towards a homogeneous architecture for high-energy ω1i is a mean-square angular frequency (rad/s) of the i-th physics data acquisition systems”, Computer Physics process; σ i and σ ξ are the standard deviation of the i-th Communications, 153, pp.155-163, 2003. [5] S.Montebugnoli, G.Bianchi, L.Zoni, “Programmable fast process and its derivative, respectively. Namely, data acquisition system”, Memorie della Societa ∞ ∞ Astronomica Italiana, vol.10, pp.195-197, 2006. ∫ ω 2 G (ω ) dω , σ i2 = ∫ G (ω )dω . 1 1 σ ξ2 = (5) [6] Michael Rizk, Iyad Obeid, Stephen H. Callender, and 2π 2π 0 0 Patrick D. Wolf, “A single-chip signal processing and The corresponding to the average interval τ between the telemetry engine for an implantable 96-channel neural two serial activity manifestations of the i-th source (4) at the data acquisition system”, Journal of Neural Engineering, adaptive sampling [11] intensity is described as follows: 4, pp.309-321, 2007. 2 ω1iσ i [7] Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar λi = 1 τ = ⋅ , (6) and Gitta Kutyniok, “Introduction to Compressed π C2 Sensing,” in Compressed Sensing: Theory and here C2 is a constant value dependent on the frequency Applications, Y. Eldar and G. Kutyniok, eds., Cambridge characteristics of measurement object sources totality and a University Press, 2011. synchronous channel tact. [8] Rusyn B., Lutsyk O., Lukenyuk A., Pohoreliuk L., For given equal probability of positive and negative current “Lossless Image Compresion in the Remote Sensing values of a sampling error, and therefore its equal zero Applications”, Proceeding of the 2016 IEEE 1st expectation, one can write the expression for the mean square International Conference on Data Stream Mining and of the absolute value of the error, i.e., Processing DSMP, pp. 195-198, 2016. 1 [9] O.Ivakhiv, A.Kowalczyk, R. Velgan, “Intelligent ∆us2i = C22 . (7) 3 Programmable Measurement System”, Proceedings of the The frequency of an adaptive switch analising procedure is XVI IMEKO World Congress. Volume IX, topic 30 - defined by the sum of intensities from all sources of Artificial Intelligence in Measurement Techniques, measurement object [11]. This value is used for analising unit Vienna-Wien, Austria, pp.341-345, September 25-28, BAU proper operation. Thus, 2000. n [10] Orest Ivakhiv, Petro Mushenyk. Yuriy Hirnyak, ∑ 1 = λi . (8) “Intelligent Analyzing System”, Sensors & Transducers T i =1 Journal, Volume 24, Special Issue, P. 43-49, August 2013. If to take the regular type sampling interval of each source [11] Kalahnikov I.D., Stepanov V.S., Churkin A.V. Adaptive equal to the estimated by analizing unit BAU one, then after Data Gathering and Transmission Systems. Moscow: a comparison of expressions (6) and (15) it is stated that a Energiya Press, 1975, 240 p. (in Russian). sampling error is less at the adaptive serving than at the [12] I.M. Teplyakov, I.D. Kalashnikov, and B.V. Roshchin, regular one (in π 2 times). Satellite Data Transmission Radio Links. Moscow: Sovetskoe Radio Press, 1975 (in Russian). IV. CONCLUSION [13] O.Ivakhiv, A.Kowalczyk, R.Viblii, “Intelligent The regular serving procedure [12,13] is based on the Measuring System Simulation”, Book of Abstracts. 16-th number of everyone source activities obtained by BAU. As IMACS World Congress on Scientific Computation, well as these results can be used for the entropy estimation of Applied Mathematics and Simulation (Lausanne - object state [14]. Switzerland, August 21-25, 2000), Ecole Polytechnique REFERENCES Federale de Lausanne, p.460. [14] Roman Velgan, Yuriy Hirniak, Orest Ivakhiv, Petro [1] B. Höfig; P. Eichinger; C. Richter, “Education 4.0 for Mushenyk, Maksym Oleksiv. Entropy Estimation of Mechatronics – Agile and Smart”, Proceedings of the 18- Investigated Object State, Proceedings of the XIIIth th International Conference on Research and Education International Conference Perspective Technologies and in Mechatronics (REM’2017), Wolfenbuettel, Germany, Methods in MEMS Design (MEMSTECH). Polyana, September 14 – 15, 2017, Wolfenbuettel: Published by Ukraine, p.132-135, April 20-23, 2017. ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic