Search results for: Ambient Intelligence

Authors: C. Adam, G. Henri, T. Levent, J.-B. Mauro, A. -L. Mayet

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This paper is concerning the application of a deterministic decisional pattern to a multi-agent system which would provide intelligence to a distributed energy smart grid at local consumer level. Development of multi-agent application involves agent specifications, analysis, design and realization. It can be implemented by following several decisional patterns. The purpose of present article is to suggest a new approach to control the smart grid system in a decentralized competitive approach. The proposed algorithmic solution results from a deterministic dichotomous approach based on environment observation. It uses an iterative process to solve automatic learning problems. Through memory of collected past tries, the algorithm monotonically converges to very steep system operation point in attraction basin resulting from weak system nonlinearity. In this sense, system is given by (local) constitutive elementary rules the intelligence of its global existence so that it can self-organize toward optimal operating sequence.

Keywords: Decentralized Competitive System, Distributed Smart Grid, Multi-Agent System

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Authors: Z. Večeřa, P. Mikuška, J. Kellner, J. Navrátil, A. Langerová

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The submitted paper deals with the problems of trapping and enriching the gases and aerosols of the substances to be determined in the ambient atmosphere. Further, the paper is focused on the working principle of the miniaturized portable continuous concentrator we have designed and the possibilities of its application in air sampling and accumulation of organic and inorganic substances with which the air is contaminated. The stress is laid on trapping vapours and aerosols of solid substances with the comparatively low vapour tension such as explosive compounds.

Keywords: Detectors of explosives, portable continuousconcentrator, misuse of explosive, terrorism.

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Authors: N. Khon, A. Kim, T. Mukhitdinova

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The aim of this research was to reveal the link between mental variables, such as spatial abilities, memory, intellect and professional experience of drivers. Participants were allocated to four groups: no experience, inexperienced, skilled and professionals (total 85 participants). The level of ability for spatial navigation and indicator of nonverbal memory grow along the process of accumulation of driving experience. At high levels of driving experience, this tendency is especially noticeable. The professionals having personal achievements in driving (racing) differ from skilled drivers in better feeling of direction, which is specific for them not just in a short-term situation of an experimental task, but also in life-size perspective. The level of ability of mental rotation does not grow with the growth of driving experience, which confirms the multiple intelligence theory according to which spatial abilities represent specific, other than logical intelligence type of intellect. The link between spatial abilities, memory, intellect and professional experience of drivers seems to be different relating spatial navigation or mental rotation as different kinds of spatial abilities.

Keywords: Memory, spatial abilities, intellect, drivers.

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Authors: Gunther Reinhart, Marwan Radi

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Since the 1940s, many promising telepresence research results have been obtained. However, telepresence technology still has not reached industrial usage. As human intelligence is necessary for successful execution of most manual assembly tasks, the ability of the human is hindered in some cases, such as the assembly of heavy parts of small/medium lots or prototypes. In such a case of manual assembly, the help of industrial robots is mandatory. The telepresence technology can be considered as a solution for performing assembly tasks, where the human intelligence and haptic sense are needed to identify and minimize the errors during an assembly process and a robot is needed to carry heavy parts. In this paper, preliminary steps to integrate the telepresence technology into industrial robot systems are introduced. The system described here combines both, the human haptic sense and the industrial robot capability to perform a manual assembly task remotely using a force feedback joystick. Mapping between the joystick-s Degrees of Freedom (DOF) and the robot-s ones are introduced. Simulation and experimental results are shown and future work is discussed.

Keywords: Assembly, Force Feedback, Industrial Robot, Teleassembly, Telepresence.

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Authors: S. X. Sun, Q. Wang

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With the rapid development of industrialization, countries in different stages of development in the world have gradually begun to pay attention to the impact of air pollution on health and the environment. Air control in developed countries is an effective reference for air control in developing countries. Artificial intelligence and other technologies also play a positive role in the prediction of air pollution. By comparing the annual changes of pollution in London and Beijing, this paper concludes that the pollution in developed cities is relatively low and stable, while the pollution in Beijing is relatively heavy and unstable, but is clearly improving. In addition, by analyzing the changes of major pollutants in Beijing in the past eight years, it is concluded that all pollutants except O3 show a significant downward trend. In addition, all pollutants except O3 have certain correlation. For example, PM10 and PM2.5 have the greatest influence on air quality index (AQI). Python, which is commonly used by artificial intelligence, is used as the main software to establish two models, support vector machine (SVM) and linear regression. By comparing the two models under the same conditions, it is concluded that SVM has higher accuracy in pollution prediction. The results of this study provide valuable reference for pollution control and prediction in developing countries.

Keywords: Air pollution, particulate matter, AQI, correlation coefficient, air pollution prediction.

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Authors: Sung Ho Ha, Seong Hyeon Joo, Eun Kyung Kwon

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Until recently, researchers have developed various tools and methodologies for effective clinical decision-making. Among those decisions, chest pain diseases have been one of important diagnostic issues especially in an emergency department. To improve the ability of physicians in diagnosis, many researchers have developed diagnosis intelligence by using machine learning and data mining. However, most of the conventional methodologies have been generally based on a single classifier for disease classification and prediction, which shows moderate performance. This study utilizes an ensemble strategy to combine multiple different classifiers to help physicians diagnose chest pain diseases more accurately than ever. Specifically the ensemble strategy is applied by using the integration of decision trees, neural networks, and support vector machines. The ensemble models are applied to real-world emergency data. This study shows that the performance of the ensemble models is superior to each of single classifiers.

Keywords: Diagnosis intelligence, ensemble approach, data mining, emergency department

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Authors: Xiao Yihong, Yu Kexin, Burra Venkata Durga Kumar

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With the accelerating process of urbanization, the problem of urban road congestion is becoming more and more serious. Intelligent transportation system combining distributed and artificial intelligence has become a research hotspot. As the core development direction of the intelligent transportation system, Cooperative Intelligent Transportation System (C-ITS) integrates advanced information technology and communication methods and realizes the integration of human, vehicle, roadside infrastructure and other elements through the multi-agent distributed system. By analyzing the system architecture and technical characteristics of C-ITS, the paper proposes a distributed multi-agent C-ITS. The system consists of Roadside Subsystem, Vehicle Subsystem and Personal Subsystem. At the same time, we explore the scalability of the C-ITS and put forward incorporating local rewards in the centralized training decentralized execution paradigm, hoping to add a scalable value decomposition method. In addition, we also suggest introducing blockchain to improve the safety of the traffic information transmission process. The system is expected to improve vehicle capacity and traffic safety.

Keywords: Distributed system, artificial intelligence, multi-agent, Cooperative Intelligent Transportation System.

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Authors: Goh Yongli, Ooi Shih Yin, Pang Ying Han

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Unintentional falls are rife throughout the ages and have been the common factor of serious or critical injuries especially for the elderly society. Fortunately, owing to the recent rapid advancement in technology, fall detection system is made possible, enabling detection of falling events for the elderly, monitoring the patient and consequently provides emergency support in the event of falling. This paper presents a review of 3 main categories of fall detection techniques, ranging from year 2005 to year 2010. This paper will be focusing on discussing the techniques alongside with summary and conclusion for them.

Keywords: State of the art, fall detection, wearable devices, ambient analyser, motion detection.

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Authors: K.Manimala, Dr.K.Selvi, R.Ahila

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Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction of such equipment may lead to loss of production or disruption of critical services resulting in huge financial and other losses. It is therefore necessary that critical loads be supplied with electricity of acceptable quality. Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the problem. In this work two classes of AI methods for Power quality data mining are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs in two critical respects: SVMs train and run an order of magnitude faster; and SVMs give higher classification accuracy.

Keywords: back propagation network, power quality, probabilistic neural network, radial basis function support vector machine

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Authors: R. Balamurugan, A. M. Natarajan, K. Premalatha

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Microarray gene expression data play a vital in biological processes, gene regulation and disease mechanism. Biclustering in gene expression data is a subset of the genes indicating consistent patterns under the subset of the conditions. Finding a biclustering is an optimization problem. In recent years, swarm intelligence techniques are popular due to the fact that many real-world problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to find an optimization technique whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. In this paper, the algorithmic concepts of the Particle Swarm Optimization (PSO), Shuffled Frog Leaping (SFL) and Cuckoo Search (CS) algorithms have been analyzed for the four benchmark gene expression dataset. The experiment results show that CS outperforms PSO and SFL for 3 datasets and SFL give better performance in one dataset. Also this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component.

Keywords: Particle swarm optimization, Shuffled frog leaping, Cuckoo search, biclustering, gene expression data.

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Authors: Ahmad Sharieh, R Bremananth

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Artificial Intelligence based gaming is an interesting topic in the state-of-art technology. This paper presents an automation of a tradition Omani game, called Al-Hawalees. Its related issues are resolved and implemented using artificial intelligence approach. An AI approach called mini-max procedure is incorporated to make a diverse budges of the on-line gaming. If number of moves increase, time complexity will be increased in terms of propositionally. In order to tackle the time and space complexities, we have employed a back propagation neural network (BPNN) to train in off-line to make a decision for resources required to fulfill the automation of the game. We have utilized Leverberg- Marquardt training in order to get the rapid response during the gaming. A set of optimal moves is determined by the on-line back propagation training fashioned with alpha-beta pruning. The results and analyses reveal that the proposed scheme will be easily incorporated in the on-line scenario with one player against the system.

Keywords: Artificial neural network, back propagation gaming, Leverberg-Marquardt, minimax procedure.

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Authors: Giovanni Luca Masala

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Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, dissimilarity

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Authors: Anupam Bhatia

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Upon reviewing the literature and the pragmatic work done in the field of E- textiles, it is observed that the applications of wearable technologies have found a steady growth in the field of military, medical, industrial, sports; whereas fashion is at a loss to know how to treat this technology and bring it to market. The purpose of this paper is to understand the practical issues of integration of electronics in garments; cutting patterns for mass production, maintaining the basic properties of textiles and daily maintenance of garments that hinder the wide adoption of interactive fabric technology within Fashion and leisure wear. To understand the practical hindrances an experimental and laboratory approach is taken. “Techno Meets Fashion” has been an interactive fashion project where sensor technologies have been embedded with textiles that result in set of ensembles that are light emitting garments, sound sensing garments, proximity garments, shape memory garments etc. Smart textiles, especially in the form of textile interfaces, are drastically underused in fashion and other lifestyle product design. Clothing and some other textile products must be washable, which subjects to the interactive elements to water and chemical immersion, physical stress, and extreme temperature. The current state of the art tends to be too fragile for this treatment. The process for mass producing traditional textiles becomes difficult in interactive textiles. As cutting patterns from larger rolls of cloth and sewing them together to make garments breaks and reforms electronic connections in an uncontrolled manner. Because of this, interactive fabric elements are integrated by hand into textiles produced by standard methods. The Arduino has surely made embedding electronics into textiles much easier than before; even then electronics are not integral to the daily wear garments. Soft and flexible interfaces of MEMS (micro sensors and Micro actuators) can be an option to make this possible by blending electronics within E-textiles in a way that’s seamless and still retains functions of the circuits as well as the garment. Smart clothes, which offer simultaneously a challenging design and utility value, can be only mass produced if the demands of the body are taken care of i.e. protection, anthropometry, ergonomics of human movement, thermo- physiological regulation.

Keywords: Ambient Intelligence, Proximity Sensors, Shape Memory Materials, Sound sensing garments, Wearable Technology.

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Authors: Akram Hosseinian, Hourieh Rezaei, Ali Reza Mahjoub

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Nanostructured Iron Oxide with different morphologies of rod-like and granular have been suc-cessfully prepared via a solid-state reaction in the presence of NaCl, NaBr, NaI and NaN3, respectively. The added salts not only prevent a drastic increase in the size of the products but also provide suitable conditions for the oriented growth of primary nanoparticles. The formation mechanisms of these materials by solid-state reaction at ambient temperature are proposed. The photocatalytic experiments for congo red (CR) have demonstrated that the mixture of α-Fe2O3 and Fe3O4 nanostructures were more efficient than α-Fe2O3 nanostructures.

Keywords: Nano, Iron Oxide, Solid-State, Halide salts, Congored

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Authors: J.R. Quevedo, E. Montañés, J. Ranilla, A. Bahamonde

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The new framework the Higher Education is immersed in involves a complete change in the way lecturers must teach and students must learn. Whereas the lecturer was the main character in traditional education, the essential goal now is to increase the students' participation in the process. Thus, one of the main tasks of lecturers in this new context is to design activities of different nature in order to encourage such participation. Seminars are one of the activities included in this environment. They are active sessions that enable going in depth into specific topics as support of other activities. They are characterized by some features such as favoring interaction between students and lecturers or improving their communication skills. Hence, planning and organizing strategic seminars is indeed a great challenge for lecturers with the aim of acquiring knowledge and abilities. This paper proposes a method using Artificial Intelligence techniques to obtain student profiles from their marks and preferences. The goal of building such profiles is twofold. First, it facilitates the task of splitting the students into different groups, each group with similar preferences and learning difficulties. Second, it makes it easy to select adequate topics to be a candidate for the seminars. The results obtained can be either a guarantee of what the lecturers could observe during the development of the course or a clue to reconsider new methodological strategies in certain topics.

Keywords: artificial intelligence, clustering, organizingseminars, student profile

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Authors: Prakash Singh

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For schools to be desirable places in which to work, it is necessary for principals to recognise their teachers’ emotions, and be sensitive to their needs. This necessitates that principals are capable to correctly identify their emotionally intelligent behaviours (EIBs) they need to use in order to be successful leaders. They also need to have knowledge of their emotional intelligence and be able to identify the factors and situations that evoke emotion at an interpersonal level. If a principal is able to do this, then the control and understanding of emotions and behaviours of oneself and others could improve vastly. This study focuses on the interpersonal EIBS of principals affecting the job satisfaction of teachers. The correlation coefficients in this quantitative study strongly indicate that there is a statistical significance between the respondents’ level of job satisfaction, the rating of their principals’ EIBs and how they believe their principals’ EIBs will affect their sense of job satisfaction. It can be concluded from the data obtained in this study that there is a significant correlation between the sense of job satisfaction of teachers and their principals’ interpersonal EIBs. This means that the more satisfied a teacher is at school, the more appropriate and meaningful a principal’s EIBs will be. Conversely, the more dissatisfied a teacher is at school the less appropriate and less meaningful a principal’s interpersonal EIBs will be. This implies that the leaders’ EIBs can be construed as one of the major factors affecting the job satisfaction of employees.

Keywords: Emotional intelligence, teachers’ emotions, teachers’ job satisfaction, principals’ emotionally intelligent behaviours.

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Authors: Saranya Chularee, Tawa Chularee

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Successful intelligence (SI) is the integrated set of the ability needed to attain success in life, within individual-s sociocultural context. People are successfully intelligent by recognizing their strengths and weaknesses. They will find ways to strengthen their weakness and maintain their strength or even improve it. SI people can shape, select, and adapt to the environments by using balance of higher-ordered thinking abilities including; critical, creative, and applicative. Aims: The purposes of this study were to; 1) develop curriculum that promotes SI for nursing students, and 2) study the effectiveness of the curriculum development. Method: Research and Development was a method used for this study. The design was divided into two phases; 1) the curriculum development which composed of three steps (needs assessment, curriculum development and curriculum field trail), and 2) the curriculum implementation. In this phase, a pre-experimental research design (one group pretest-posttest design) was conducted. The sample composed of 49 sophomore nursing students of Boromarajonani College of Nursing, Surin, Thailand who enrolled in Nursing care of Health problem course I in 2011 academic year. Data were carefully collected using 4 instruments; 1) Modified essay questions test (MEQ) 2) Nursing Care Plan evaluation form 3) Group processing observation form (α = 0.74) and 4) Satisfied evaluation form of learning (α = 0.82). Data were analyzed using descriptive statistics and content analysis. Results: The results revealed that the sample had post-test average score of SI higher than pre-test average score (mean difference was 5.03, S.D. = 2.84). Fifty seven percentages of the sample passed the MEQ posttest at the criteria of 60 percentages. Students demonstrated the strategies of how to develop nursing care plan. Overall, students- satisfaction on teaching performance was at high level (mean = 4.35, S.D. = 0.46). Conclusion: This curriculum can promote the attribute of characteristic of SI person and was highly required to be continued.

Keywords: Curriculum Development, Nursing Education, Successful Intelligence, Thinking ability.

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Authors: Shashikant R. Kuchekar, Haribhau R. Aher, Priti M. Dhage

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The removal vanadium from aqueous solution using natural exchanger was investigated. The effects of pH, contact time and exchanger dose were studied at ambient temperature (25 ⁰C ± 2 ⁰C). The equilibrium process was described by the Langmuir isotherm model with adsorption capacity for vanadium. The natural exchanger i.e. tamarindus seeds powder was treated with formaldehyde and sulpuric acid to increase the adsorptivity of metals. The maximum exchange level was attained as 80.1% at pH 3 with exchanger dose 5 g and contact time 60 min. Method is applied for removal of vanadium from industrial effluents.

Keywords: Industrial effluent, natural ion exchange, Tamarindus indica, vanadium.

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Authors: Ben Khayut, Lina Fabri, Maya Avikhana

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The modern Artificial Narrow Intelligence (ANI) models cannot: a) independently, situationally, and continuously function without of human intelligence, used for retraining and reprogramming the ANI’s models, and b) think, understand, be conscious, and cognize under uncertainty and changing of the environmental objects. To eliminate these shortcomings and build a new generation of Artificial Intelligence systems, the paper proposes a Conception, Model, and Method of Computational Psycholinguistic Cognitive Situational-Fuzzy Self-Controlled Brain and Mind System (CPCSFSCBMSUU). This system uses a neural network as its computational memory, and activates functions of the perception, identification of real objects, fuzzy situational control, and forming images of these objects. These images and objects are used for modeling their psychological, linguistic, cognitive, and neural values of properties and features, the meanings of which are identified, interpreted, generated, and formed taking into account the identified subject area, using the data, information, knowledge, accumulated in the Memory. The functioning of the CPCSFSCBMSUU is carried out by its subsystems of the: fuzzy situational control of all processes, computational perception, identifying of reactions and actions, Psycholinguistic Cognitive Fuzzy Logical Inference, Decision Making, Reasoning, Systems Thinking, Planning, Awareness, Consciousness, Cognition, Intuition, and Wisdom. In doing so are performed analysis and processing of the psycholinguistic, subject, visual, signal, sound and other objects, accumulation and using the data, information and knowledge of the Memory, communication, and interaction with other computing systems, robots and humans in order of solving the joint tasks. To investigate the functional processes of the proposed system, the principles of situational control, fuzzy logic, psycholinguistics, informatics, and modern possibilities of data science were applied. The proposed self-controlled system of brain and mind is oriented on use as a plug-in in multilingual subject applications.

Keywords: Computational psycholinguistic cognitive brain and mind system, situational fuzzy control, uncertainty, AI.

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Authors: Md. Harunur Rashid, S. M. Kamal Uddin, Sobura khatun

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The performance of mortar subjected to high temperature and cooled in normal ambient temperature was examined in the laboratory to comply with the situation of burning & cooling of a structure. Four series of cubical (5 X 5 X 5 cm) mortar specimens were made from OPC, and partial replacement (10, 15, 20, 25 & 30%) of OPC by Rice Husk Ash (RHA) produced in the uncontrolled environment. These specimens were heated in electric furnace to 200, 300, 400, 500 and 7000C. The specimens were kept in normal room temperature for cooling. They were then tested for mechanical properties and the results shows that particular 20% RHA mixed mortar shows better fire performance.

Keywords: Fire performance, Rice Husk

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Authors: Doaa M. Atia, Faten H. Fahmy, Ninet M. Ahmed, Hassen T. Dorrah

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During the past decade, pond aeration systems have been developed which will sustain large quantities of fish and invertebrate biomass. Dissolved Oxygen (DO) is considered to be among the most important water quality parameters in fish culture. Fishponds in aquaculture farms are usually located in remote areas where grid lines are at far distance. Aeration of ponds is required to prevent mortality and to intensify production, especially when feeding is practical, and in warm regions. To increase pond production it is necessary to control dissolved oxygen. Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. This paper presents a new design of diffused aeration system using fuel cell as a power source. Also fuzzy logic control Technique (FLC) is used for controlling the speed of air flow rate from the blower to air piping connected to the pond by adjusting blower speed. MATLAB SIMULINK results show high performance of fuzzy logic control (FLC).

Keywords: aeration system, Fuel cell, Artificial intelligence (AI) techniques, fuzzy logic control

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Authors: Adrian T. Pleșca

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In this paper a three dimensional thermal model of a sliding contact system is proposed for both steady-state or transient conditions. The influence of contact force, electric current and ambient temperature on the temperature distribution, has been investigated. A thermal analysis of the different type of the graphite material of fixed electric contact and its influence on contact system temperature rise, has been performed. To validate the three dimensional thermal model, some experimental tests have been done. There is a good correlation between experimental and simulation results.

Keywords: Sliding electric contact, temperature distribution, thermal analysis.

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Authors: L. K. Davis

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The effects of the evolution force are observable in nature at all structural levels ranging from small molecular systems to conversely enormous biospheric systems. However, the evolution force and work associated with formation of biological structures has yet to be described mathematically or theoretically. In addressing the conundrum, we consider evolution from a unique perspective and in doing so we introduce the “Fundamental Theory of the Evolution Force: FTEF”. We utilized synthetic evolution artificial intelligence (SYN-AI) to identify genomic building blocks and to engineer 14-3-3 ζ docking proteins by transforming gene sequences into time-based DNA codes derived from protein hierarchical structural levels. The aforementioned served as templates for random DNA hybridizations and genetic assembly. The application of hierarchical DNA codes allowed us to fast forward evolution, while dampening the effect of point mutations. Natural selection was performed at each hierarchical structural level and mutations screened using Blosum 80 mutation frequency-based algorithms. Notably, SYN-AI engineered a set of three architecturally conserved docking proteins that retained motion and vibrational dynamics of native Bos taurus 14-3-3 ζ.

Keywords: 14-3-3 docking genes, synthetic protein design, time based DNA codes, writing DNA code from scratch.

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Authors: A. Mohamed Mydeen, Pallapa Venkataram

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The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.

Keywords: Generic knowledge representation, toolkit, toolroom, pervasive computing.

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: Artificial Intelligence, machine learning, deep learning, convolutional neural networks.

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Authors: Guenther Schuh, Christina Koenig, Nico Schoen, Markus Wellensiek

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Identification and selection of appropriate product and manufacturing technologies are key factors for competitiveness and market success of technology-based companies. Therefore, many companies perform technology intelligence (TI) activities to ensure the identification of evolving technologies at the right time. Technology monitoring is one of the three base activities of TI, besides scanning and scouting. As the technological progress is accelerating, more and more technologies are being developed. Against the background of limited resources it is therefore necessary to focus TI activities. In this paper we propose a concept for defining appropriate search fields for technology monitoring. This limitation of search space leads to more concentrated monitoring activities. The concept will be introduced and demonstrated through an anonymized case study conducted within an industry project at the Fraunhofer Institute for Production Technology IPT. The described concept provides a customized monitoring approach, which is suitable for use in technology-oriented companies. It is shown in this paper that the definition of search fields and search tasks are suitable methods to define topics of interest and thus to align monitoring activities. Current as well as planned product, production and material technologies and existing skills, capabilities and resources form the basis for derivation of relevant search areas. To further improve the concept of technology monitoring the proposed concept should be extended during future research e.g. by the definition of relevant monitoring parameters.

Keywords: Monitoring radar, search field, technology intelligence, technology monitoring.

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Authors: Saria Abed, Tahar Khir, Ammar Ben Brahim

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A thermo-exergy optimization of a gas turbine cycle with two different regenerator designs is established. A comparison was made between the performance of the two regenerators and their roles in improving the cycle efficiencies. The effect of operational parameters (the pressure ratio of the compressor, the ambient temperature, excess of air, geometric parameters of the regenerators, etc.) on thermal efficiencies, the exergy efficiencies, and irreversibilities were studied using thermal balances and quantitative exegetic equilibrium for each component and for the whole system. The results are given graphically by using the EES software, and an appropriate discussion and conclusion was made.

Keywords: Exergy efficiency, gas turbine, heat transfer, irreversibility, optimization, regenerator, thermal efficiency.

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Authors: Dipo Dunsin, Mohamed C. Ghanem, Karim Ouazzane

Abstract:

Digital investigators often have a hard time spotting evidence in digital information. It has become hard to determine which source of proof relates to a specific investigation. A growing concern is that the various processes, technology, and specific procedures used in the digital investigation are not keeping up with criminal developments. Therefore, criminals are taking advantage of these weaknesses to commit further crimes. In digital forensics investigations, artificial intelligence (AI) is invaluable in identifying crime. Providing objective data and conducting an assessment is the goal of digital forensics and digital investigation, which will assist in developing a plausible theory that can be presented as evidence in court. This research paper aims at developing a multiagent framework for digital investigations using specific intelligent software agents (ISAs). The agents communicate to address particular tasks jointly and keep the same objectives in mind during each task. The rules and knowledge contained within each agent are dependent on the investigation type. A criminal investigation is classified quickly and efficiently using the case-based reasoning (CBR) technique. The proposed framework development is implemented using the Java Agent Development Framework, Eclipse, Postgres repository, and a rule engine for agent reasoning. The proposed framework was tested using the Lone Wolf image files and datasets. Experiments were conducted using various sets of ISAs and VMs. There was a significant reduction in the time taken for the Hash Set Agent to execute. As a result of loading the agents, 5% of the time was lost, as the File Path Agent prescribed deleting 1,510, while the Timeline Agent found multiple executable files. In comparison, the integrity check carried out on the Lone Wolf image file using a digital forensic tool kit took approximately 48 minutes (2,880 ms), whereas the MADIK framework accomplished this in 16 minutes (960 ms). The framework is integrated with Python, allowing for further integration of other digital forensic tools, such as AccessData Forensic Toolkit (FTK), Wireshark, Volatility, and Scapy.

Keywords: Artificial intelligence, computer science, criminal investigation, digital forensics.

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Authors: Dhurgham Al-Karawi, Nisreen Polus, Shakir Al-Zaidi, Sabah Jassim

Abstract:

The management of COVID-19 patients based on chest imaging is emerging as an essential tool for evaluating the spread of the pandemic which has gripped the global community. It has already been used to monitor the situation of COVID-19 patients who have issues in respiratory status. There has been increase to use chest imaging for medical triage of patients who are showing moderate-severe clinical COVID-19 features, this is due to the fast dispersal of the pandemic to all continents and communities. This article demonstrates the development of machine learning techniques for the test of COVID-19 patients using Chest X-Ray (CXR) images in nearly real-time, to distinguish the COVID-19 infection with a significantly high level of accuracy. The testing performance has covered a combination of different datasets of CXR images of positive COVID-19 patients, patients with viral and bacterial infections, also, people with a clear chest. The proposed AI scheme successfully distinguishes CXR scans of COVID-19 infected patients from CXR scans of viral and bacterial based pneumonia as well as normal cases with an average accuracy of 94.43%, sensitivity 95%, and specificity 93.86%. Predicted decisions would be supported by visual evidence to help clinicians speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.

Keywords: COVID-19, chest x-ray scan, artificial intelligence, texture analysis, local binary pattern transform, Gabor filter.

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Authors: Adrian T.Pleşca

Abstract:

In this paper a three dimensional thermal model of high breaking capacity fuse with unequal fuse links is proposed for both steady-state or transient conditions. The influence of ambient temperature and electric current on the temperature distribution inside the fuse, has been investigated. A thermal analysis of the unbalanced distribution of the electric current through the fuse elements and their influence on fuse link temperature rise, has been performed. To validate the three dimensional thermal model, some experimental tests have been done. There is a good correlation between experimental and simulation results.

Keywords: Electric fuse, fuse links, temperature distribution, thermal analysis.

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