Complex Information Processing Systems Research Articles

MBSE Model Management Pain Points – Wait, this looks familiar!

AbstractIt has been almost 20 years since INCOSE and the OMG kicked off an effort to create a standardized model‐based systems engineering modeling language. That effort culminated in 2006 with the creation of the Systems Modeling language (SysML). Over the past several years, numerous industries have increased their adoption of SysML and MBSE as a core practice within their engineering lifecycles. However, they have not achieved many of the originally envisioned benefits. On closer inspection, many of the challenges and barriers with which MBSE practitioners are currently struggling are remarkably similar to the problems seen in large DoD software programs from the 1970s and 80s and were resolved decades ago by the software development community regarding scale and complexity. System models are becoming larger and more complex. Yet, in this expanding and maturing MBSE environment, projects continue to experience problems with model integration, repository performance and model lifecycle management. It is time to go back and take another look at how the software community attacked problems of extreme scale.The similarity between MBSE's model management pain points and those experienced years ago by the software industry is not unexpected. Adoption of MBSE requires the replacement of the largely manual, document‐based engineering processes with a complex engineering information processing system. Unlike documents, the models produced by that system are living artifacts that require management over the project lifecycle and which have all the characteristics and complexity of software.This paper presents a framework for MBSE planning and model lifecycle management based on the key practices from Systems Engineering and Software Engineering to provide an actionable set of best practices that can be applied today to address current MBSE lifecycle management issues. We have organized these best practices around three key MBSE Model Management Imperatives.

FuSE Agility as a Foundation for Sound MBSE Lifecycle Management

ABSTRACTOver the past several years, numerous industries have increased their adoption of the systems modeling language (SysML®) and model‐based systems engineering (MBSE) as a core practice within their engineering lifecycles. However, the introduction of SysML and MBSE methodologies has not yet yielded many of the originally envisioned benefits. System models are becoming larger and more complex and many large MBSE projects continue to experience problems with model integration, repository performance, and model lifecycle management. The root cause is the failure to recognize the MBSE digital environment as a complex engineering information processing system that requires the same rigor and development processes as the system‐of‐interest (SoI) it is designing. This article describes how three future of systems engineering (FuSE) agility foundation concepts (system of innovation, effective stakeholder engagement, and continuous integration) directly address some of the problems seen in adoption, deployment, and sustainment of the MBSE digital environment as an SoI.

Functional brain imaging and central control of the bladder in health and disease.

Central control of the bladder is a complex process. With the development of functional imaging technology and analysis methods, research on brain-bladder control has become more in-depth. Here, we review previous functional imaging studies and combine our latest findings to discuss brain regions related to bladder control, interactions between these regions, and brain networks, as well as changes in brain function in diseases such as urgency urinary incontinence, idiopathic overactive bladder, interstitial cystitis/bladder pain syndrome, urologic chronic pain syndrome, neurogenic overactive bladder, and nocturnal enuresis. Implicated brain regions include the pons, periaqueductal grey, thalamus, insula, prefrontal cortex, cingulate cortex, supplementary motor area, cerebellum, hypothalamus, basal ganglia, amygdala, and hippocampus. Because the brain is a complex information transmission and processing system, these regions do not work in isolation but through functional connections to form a number of subnetworks to achieve bladder control. In summarizing previous studies, we found changes in the brain functional connectivity networks related to bladder control in healthy subjects and patients involving the attentional network, central executive network or frontoparietal network, salience network, interoceptive network, default mode network, sensorimotor network, visual network, basal ganglia network, subcortical network, cerebella, and brainstem. We extend the working model proposed by Griffiths et al. from the brain network level, providing insights for current and future bladder-control research.

Multifunctional Logic‐in‐Memory Cell Based on Wafer‐Scale MoS2 Thin Films Prepared by Atomic Layer Deposition

Large‐volume data storage and high‐efficiency data processing have been greatly urged by the fast development in artificial intelligence and internet of things involving novel semiconductor materials. Logic‐in‐memory has provided a promising route to address the processing/storage bottleneck in advanced computing systems. Herein, a logic‐in‐memory design based on wafer‐scale MoS2 floating‐gate field‐effect transistor (FGFET) arrays is demonstrated. Multiple logic states and over 10 V memory window are achieved through electrical gate modulation, which further enables in‐memory computing including logic functions of inverter and two‐input NAND gates. Such robust and stable logic‐in‐memory is ascribed to the highly uniform wafer‐scale MoS2 thin film prepared by atomic layer deposition (ALD)‐based synthesis and the reproducible electrical performance of the device arrays. As both memory and computing units are based on floating‐gate FET architecture with area efficiency, the proposed logic‐in‐memory cell can accomplish multifunctional tasks with fewer transistors, suggesting a potential application in high‐density and complex information processing and data storage systems.

Complexing of information channels of UAV detection and observation systems from the statistic solutions theory standpoint

Currently, unmanned aerial vehicles (UAVs) provide a wide range of useful tasks for humanity, but, on the other hand, they pose a serious threat in economic, military and other areas of human activity. Difficulties in observing UAVs using modern technical means, as well as their relatively low cost, lead to an expansion of the scope of UAVs based illegal actions. Therefore, the protection of various objects against UAVs is a serious scientific and technical task of today.
 Since the possibilities of the known methods of UAV detection are different, the joint use of systems of different types is realized in practice nowadays, in order to increase the informativeness of the obtained data by their joint (complex) processing.
 The number of publications in this field is constantly increasing, and considerable attention is paid to integrated systems built on the basis of various physical sensors. However, the efficiency of multi-sensor systems with integrated processing of the output signals of the channels in practice remains insufficient.
 This article is devoted to the study of methods for the synthesis of new, more efficient algorithms for complexing radar, acoustic, optical and infrared information channels of integrated UAV detection and recognition systems, which are performed from the standpoint of statistical theory of radio system optimization.
 This approach allows synthesizing the optimal (according to the selected quality criterion) complex information processing system, which ensures obtaining the maximum amount of information from the vector process observed at the inputs of information channels. There shown the possibility of constructing an optimal UAV detector with the use of the late strategy of combining information at the level of decisions made in individual channels of the system.

Principles of synthetic biology.

In synthetic biology, biological cells and processes are dismantled and reassembled to make novel systems that do useful things. Designs are encoded by deoxyribonucleic acid (DNA); DNA makes biological (bio-)parts; bioparts are combined to make devices; devices are built into biological systems. Computers are used at all stages of the Design–Build–Test–Learn cycle, from mathematical modelling through to the use of robots for the automation of assembly and experimentation. Synthetic biology applies engineering principles of standardisation, modularity, and abstraction, enabling fast prototyping and the ready exchange of designs between synthetic biologists working around the world. Like toy building blocks, compatible modular designs enable bioparts to be combined and optimised easily; biopart specifications are shared in open registries. Synthetic biology is made possible due to major advances in DNA sequencing and synthesis technologies, and through knowledge gleaned in the field of systems biology. Systems biology aims to understand biology across scales, from the molecular and cellular, up to tissues and organisms, and describes cells as complex information-processing systems. By contrast, synthetic biology seeks to design and build its own systems. Applications of synthetic biology are wide-ranging but include impacting healthcare to improve diagnosis and make better treatments for disease; it seeks to improve the environment by finding novel ways to clean up pollution, make industrial processes for chemical synthesis sustainable, and remove the need for damaging farming practices by making better fertilisers. Synthetic biology has the potential to change the way we live and help us to protect the future of our planet.

ПРИМЕНЕНИЕ НЕЙРОННЫХ СЕТЕЙ ДЛЯ РАСПОЗНАВАНИЯ ПРИНЦИПИАЛЬНЫХ УСЛОВНО-ГРАФИЧЕСКИХ ЭЛЕКТРИЧЕСКИХ ОБОЗНАЧЕНИЙ

The article discusses the issue of using artificial neural networks for recognizing the conditionally graphical designations of electrical engineering, in particular, the convolutional neural networks and the R-CNN object recognition model, which is most suitable for solving the task at hand. Recognition of images of a specific picture is a task set for the complex information processing systems, as well as control and decision-making systems. The classification of various technological or natural objects, analog and digital signals is developed by a set of specific characteristics and properties. Defining the type and features of an object finds its application in different branches of science: machine learning, diagnostics, meteorology, video surveillance and security systems, in virtual reality systems and image search. However, research has not yet been carried out for solving the applied problems and achieving the required parameters (e.g. in recognizing conditional graphical symbols of electrical engineering). The neural networks have been found to have the highest quality and most promising among all mathematical models and methods of pattern recognition. As for the interactivity, the output result of image recognition work is a necessary and sufficient answer, which does not have a stable work on the variability of objects within categories and their invariant transformations. The scheme of the model R-CNN has been studied in detail, the importance of the training sample and its influence on the quality of pattern recognition by the neural network have been grounded. The application of the RoI Pooling method for object recognition in the image is shown in general, due to which there have been selected several regions of interest indicated through the bounding boxes.

Parallel decomposition of control algorithms for computational processes based on the use of nondeterministic automaton logic

The paper deals with the issues of decomposition of control algorithms for the processes in parallel computing systems and the use of automaton models. When designing parallel processing systems, an important task is the formal presentation of process control algorithms since they allow achieving a packaged solution to the problems of specification, development, implementation, verification, and analysis of complex control systems, including the control of interacting processes and resources in parallel computing systems. It is especially necessary to use formal methods to verify complex information processing systems by model testing. One of the methods for the formal description of control algorithms is based on the use for these purposes of the nondeterministic automaton (NDA) logic, which is a method that allows one to present control algorithms for information processing in the form of systems of canonical equations describing all particular events implemented in the algorithm. The advantage of such a language is that all transitions in the control system are described not in terms of system states, but in terms of particular events, the simultaneous existence of which determines all states and transitions in the system; this allows avoiding a "combinatorial explosion" in the state space to the possibilities of means verification. Purpose of the paper: research of control algorithms for parallel computing systems using the NDA apparatus. The development and research object is parallel decomposition of control algorithms for parallel computing systems using automatic models.

Editors’ Comments: Should Management Theories Take Uncertainty Seriously?

Editors’ Comments: Should Management Theories Take Uncertainty Seriously?

Neglect Dyslexia in Relation to Unilateral Visuospatial Neglect: A Review

The human brain is arguably the most complex information processing system. It operates by acquiring data from the environment, recognizing patterns of events’ occurrence, anticipating their re-occurrence and in turn generating appropriate behavioral responses. Through the lenses of the free-energy principle any self-organizing system that is at equilibrium with its environment must minimize its free energy either by manipulating the environmental sensory input or by manipulating its internal states thus altering the recognition density of the outside stimuli. However, several sets of challenges interfere with the human brain's ability to learn and adapt in such a theoretically optimal fashion. These may include, and are not limited to, functional inconsistencies related to attention and memory processes, the functions of “fast” and “slow” thinking and responding, and the ability of emotional states to generate unintended behavioral outcomes that are less adaptive or inappropriate. This paper will review literature on the subject of how ideal learning viewed from the free-energy principle perspective may be affected by the above mentioned limitations and will suggest a model of information processing that may have developed as a way of overcoming these challenges. This neurobiological model stipulates that a neuronal network is formed in response to environmental input and is paralleled by at least one and possibly multiple networks that activate intrinsically and represent “virtual responses” to a situation that demands a behavioral response. This model accounts for how the brain generates a multiplicity of potential behavioral responses and may “choose” the one that seems most appropriate and also explains the uncanny ability of humans to socialize and collaborate. Implications for understanding humans’ ability to learn from others, deliberate on opposing constructs and access and utilize information outside of individual minds are also discussed.

Causal Boxes: Quantum Information-Processing Systems Closed under Composition

Complex information-processing systems, for example quantum circuits, cryptographic protocols, or multi-player games, are naturally described as networks composed of more basic information-processing systems. A modular analysis of such systems requires a mathematical model of systems that is closed under composition, i.e., a network of these objects is again an object of the same type. We propose such a model and call the corresponding systems causal boxes. Causal boxes capture superpositions of causal structures, e.g., messages sent by a causal box A can be in a superposition of different orders or in a superposition of being sent to box B and box C. Furthermore, causal boxes can model systems whose behavior depends on time. By instantiating the Abstract Cryptography framework with causal boxes, we obtain the first composable security framework that can handle arbitrary quantum protocols and relativistic protocols.

Evolution of the archaeal and mammalian information processing systems: towards an archaeal model for human disease.

Current evolutionary models suggest that Eukaryotes originated from within Archaea instead of being a sister lineage. To test this model of ancient evolution, we review recent studies and compare the three major information processing subsystems of replication, transcription and translation in the Archaea and Eukaryotes. Our hypothesis is that if the Eukaryotes arose within the archaeal radiation, their information processing systems will appear to be one of kind and not wholly original. Within the Eukaryotes, the mammalian or human systems are emphasized because of their importance in understanding health. Biochemical as well as genetic studies provide strong evidence for the functional similarity of archaeal homologs to the mammalian information processing system and their dissimilarity to the bacterial systems. In many independent instances, a simple archaeal system is functionally equivalent to more elaborate eukaryotic homologs, suggesting that evolution of complexity is likely an central feature of the eukaryotic information processing system. Because fewer components are often involved, biochemical characterizations of the archaeal systems are often easier to interpret. Similarly, the archaeal cell provides a genetically and metabolically simpler background, enabling convenient studies on the complex information processing system. Therefore, Archaea could serve as a parsimonious and tractable host for studying human diseases that arise in the information processing systems.

On the possible role of protein vibrations in information processing in the brain: three Russian dolls.

Until recently it was held that the neurocomputations conducted by the brain involved only whole neurons as the operating units. This may however represent only a part of the mechanism. This theoretical and academic position article reviews the considerable evidence that allosteric interactions between proteins (as extensively described by Fuxe et al., 2014), and in particular protein vibrations in neurons, form small scale codes that are involved as parts of the complex information processing systems of the brain. The argument is then developed to suggest that the protein allosteric and vibration codes (that operate at the molecular level) are nested within a medium scale coding system whose computational units are organelles (such as microtubules). This medium scale code is nested in turn inside a large scale coding system, whose computational units are individual neurons. The hypothesis suggests that these three levels interact vertically in both directions thus materially increasing the computational capacity of the brain. The whole hierarchy is thus similar to three nested Russian dolls. This theoretical development may be of use in the design of experiments to test it.

Predicting DNA mutations during cancer evolution.

Bio-systems are inherently complex information processing systems. Their physiological complexities limit the formulation and testing of a hypothesis for their behaviour. Our goal here was to test a computational framework utilising published data from a longitudinal study of patients with acute myeloid leukaemia (AML), whose DNA from both normal and malignant tissues were subjected to NGS analysis at various points in time. By processing the sequencing data before relapse time, we tested our framework by predicting the regions of the genome to be mutated at relapse time and, later, by comparing our results with the actual regions that showed mutations (discovered by genome sequencing at relapse time). After a detailed statistical analysis, the resulting correlation coefficient (degree of matching of proposed framework with real data) is 0.9816 ± 0.009 at 95% confidence interval. This high performance from our proposed framework opens new research opportunities for bioinformatics researchers and clinical doctors.

Shifting Gears: Seeking New Approaches for Mind/Brain Mechanisms

Using an autobiographical approach, I review several animal and human split-brain studies that have led me to change my long-term view on how best to understand mind/brain interactions. Overall, the view is consistent with the idea that complex neural systems, like other complex information processing systems, are highly modular. At the same time, how the modules come to interact and produce unitary goals is unknown. Here, I review the importance of self-cueing in that process of producing unitary goals from disparate functions. The role of self-cueing is demonstrably evident in the human neurologic patient and especially in patients with hemispheric disconnection. When viewed in the context of modularity, it may provide insights into how a highly parallel and distributed brain locally coordinates its activities to produce an apparent unitary output. Capturing and understanding how this is achieved will require shifting gears away from standard linear models and adopting a more dynamical systems view of brain function.

Modeling stand for debugging and testing of a complex information processing system with data from many different-type sources

A technique was proposed for creating a modeling stand intended for debugging and testing (autonomous and integrated) of the complex information processing system (CIPS). An approach is described that reflects the technologies and CIPS design stages using the methods of layered simulation models. The design concepts of the interactive simulation models have been also presented.

Analysis and Design of Highway Monitoring System Based on MAS

The highway monitoring system is an open, intelligent, distributed complex information processing system. Analysis of the agent (Agent), Multi-Agent System (MAS) and Agent-oriented software design (AOP), the framework of the MAS- based highway monitoring system, and its composition were analyzed, and discussed the various types of agent of the function and structure, to design a Universal Agent template. Finally, the Agent Communication Language Agent Description Language is discussed.

Design concepts of complex information processing systems with data from many different-type sources located at one or several mobile platforms

This study deals with design concepts of a complex information processing system with data from many different-type sources located at one or several mobile platforms. Approaches to the selection of CIPS structure and its hardware implementation were proposed. Optimization criteria were discussed for solving the main functional tasks of the system engaged in data acquisition and integration from information sources, target distribution and the delivery of target designation to users on shipboard.

FPGA Based Reconfigurable Computing Systems: A New Design Approach - A Review

The recent progress in modern electronic technology has enabled the implementation of complex information processing systems and created a big push towards the development of various kinds of application specific embedded and high performance systems. Design is a task that requires knowledge and creativity which are two human attributes normally considered too complex to be automated. Researchers in Artificial Intelligence have devoted a lot of work towards automating different aspects of design, but most of the current results consist of complex and expensive programs that can be easily outperformed by experienced human designers. The main goal of this review reported in this paper is to develop logic circuits which are not only fully functional, but also optimum according to some metrics. In this review, four important areas are considered. They are fpga based reconfigurable computing systems, soft computing techniques, pipelining concept and financial analysis as an application.

PLATO: Data-oriented approach to collaborative large-scale brain system modeling

The brain is a complex information processing system, which can be divided into sub-systems, such as the sensory organs, functional areas in the cortex, and motor control systems. In this sense, most of the mathematical models developed in the field of neuroscience have mainly targeted a specific sub-system. In order to understand the details of the brain as a whole, such sub-system models need to be integrated toward the development of a neurophysiologically plausible large-scale system model. In the present work, we propose a model integration library where models can be connected by means of a common data format. Here, the common data format should be portable so that models written in any programming language, computer architecture, and operating system can be connected. Moreover, the library should be simple so that models can be adapted to use the common data format without requiring any detailed knowledge on its use. Using this library, we have successfully connected existing models reproducing certain features of the visual system, toward the development of a large-scale visual system model. This library will enable users to reuse and integrate existing and newly developed models toward the development and simulation of a large-scale brain system model. The resulting model can also be executed on high performance computers using Message Passing Interface (MPI).