Search results for: human machine interface

Authors: Noriyoshi Yamauchi, Etsuo Horikawa, Takunori Tsuji

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Training by exoskeleton robot is drawing attention as a rehabilitation method for body paralysis seen in many cases, and there are many forms that assist with the myoelectric signal generated by exercise commands from the brain. Rehabilitation requires more frequent training, but it is one of the reasons that the technology is required for the identification of the myoelectric potential derivation site and attachment of the device is preventing the spread of paralysis. In this research, we focus on improving the efficiency of gait training by exoskeleton type robots, improvement of myoelectric acquisition and analysis method using active matrix sensing method, and improvement of walking rehabilitation and walking by optimization of robot control.

Keywords: active matrix sensing, brain machine interface (BMI), the central pattern generator (CPG), myoelectric signal processing, robot rehabilitation

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Authors: Neha Singh, Shristi Singh

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Accidents involving motor vehicles are more likely to cause serious injuries and fatalities. It also has a host of other perpetual issues, such as the regular loss of life and goods in accidents. To solve these issues, appropriate measures must be implemented, such as establishing an autonomous incident detection system that makes use of machine learning and artificial intelligence. In order to reduce traffic accidents, this article examines the overview of artificial intelligence and machine learning in autonomous event detection systems. The paper explores the major issues, prospective solutions, and use of artificial intelligence and machine learning in road transportation systems for minimising traffic accidents. There is a lot of discussion on additional, fresh, and developing approaches that less frequent accidents in the transportation industry. The study structured the following subtopics specifically: traffic management using machine learning and artificial intelligence and an incident detector with these two technologies. The internet of vehicles and vehicle ad hoc networks, as well as the use of wireless communication technologies like 5G wireless networks and the use of machine learning and artificial intelligence for the planning of road transportation systems, are elaborated. In addition, safety is the primary concern of road transportation. Route optimization, cargo volume forecasting, predictive fleet maintenance, real-time vehicle tracking, and traffic management, according to the review's key conclusions, are essential for ensuring the safety of road transportation networks. In addition to highlighting research trends, unanswered problems, and key research conclusions, the study also discusses the difficulties in applying artificial intelligence to road transport systems. Planning and managing the road transportation system might use the work as a resource.

Keywords: artificial intelligence, machine learning, incident detector, road transport systems, traffic management, automatic incident detection, deep learning

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Authors: Yujeong Shin, Seong Jin Cho, Jin Ho Kim

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In the textile-weaving machine, the reed is the core component to separate thousands of strands of yarn and to produce the fabric in a continuous high-speed movement. In addition, the reed affects the quality of the fiber. Therefore, the wire forming analysis of the main raw materials of the reed needs to be considered. Roll-forming is a key technology among the manufacturing process of reed wire using textile machine. A simulation of roll-forming line in accordance with the reduction rate is performed using LS-DYNA. The upper roller, fixed roller and reed wire are modeled by finite element. The roller is set to be rigid body and the wire of SUS430 is set to be flexible body. We predict the variation of the cross-sectional shape of the wire depending on the reduction ratio.

Keywords: textile machine, reed, rolling, reduction ratio, wire

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Authors: Ali Allahverdi, Harun Aydilek, Asiye Aydilek

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Minimizing the number of tardy jobs is an important factor to consider while making scheduling decisions. This is because on-time shipments are vital for lowering cost and increasing customers’ satisfaction. This paper addresses the single machine scheduling problem with the objective of minimizing the number of tardy jobs. The only known information is the lower and upper bounds for processing times, and deterministic job due dates. A dominance relation is established, and an algorithm is proposed. Several heuristics are generated from the proposed algorithm. Computational analysis indicates that the performance of one of the heuristics is very close to the optimal solution, i.e., on average, less than 1.5 % from the optimal solution.

Keywords: single machine scheduling, number of tardy jobs, heuristi, lower and upper bounds

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Authors: N. Sateesh, C. S. P. Rao, K. Satyanarayana, C. Rajashekar

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This paper proposes a development of a CAD/CAM system for complex profiles of various machine members using a synthetic curve i.e. B-spline. Conventional methods in designing and manufacturing of complex profiles are tedious and time consuming. Even programming those on a computer numerical control (CNC) machine can be a difficult job because of the complexity of the profiles. The system developed provides graphical and numerical representation B-spline profile for any given input. In this paper, the system is applicable to represent a cam profile with B-spline and attempt is made to improve the follower motion.

Keywords: plate-cams, cam profile, b-spline, computer numerical control (CNC), computer aided design and computer aided manufacturing (CAD/CAM), R-D-R-D (rise-dwell-return-dwell)

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Authors: Vani Singhal, Jitendra Parmar, Satyendra Singh Chouhan

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Unseen class discovery has now become an important part of a machine-learning algorithm to judge new classes. Unseen classes are the classes on which the machine learning model is not trained on. With the advancement in technology and AI replacing humans, the amount of data has increased to the next level. So while implementing a model on real-world examples, we come across unseen new classes. Our aim is to find the number of unseen classes by using a hierarchical-based active learning algorithm. The algorithm is based on hierarchical clustering as well as active sampling. The number of clusters that we will get in the end will give the number of unseen classes. The total clusters will also contain some clusters that have unseen classes. Instead of first discovering unseen classes and then finding their number, we directly calculated the number by applying the algorithm. The dataset used is for intent classification. The target data is the intent of the corresponding query. We conclude that when the machine learning model will encounter real-world data, it will automatically find the number of unseen classes. In the future, our next work would be to label these unseen classes correctly.

Keywords: active sampling, hierarchical clustering, open world learning, unseen class discovery

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

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Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline that ensures that data mirrors real-world settings by incorporating Gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification, and explainability in a single pipeline called DeClEx.

Keywords: machine learning, healthcare, classification, explainability

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Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

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Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

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Authors: Mohammed Arif, Abdessalam Kifouche

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Speech is a very convenient way of communication between people and machines. It conveys information about the identity of the talker. Since speaker recognition technology is increasingly securing our everyday lives, the objective of this paper is to develop two automatic text-independent speaker verification systems (TI SV) using low-level spectral features and machine learning methods. (i) The first system is based on a support vector machine (SVM), which was widely used in voice signal processing with the aim of speaker recognition involving verifying the identity of the speaker based on its voice characteristics, and (ii) the second is based on Gaussian Mixture Model (GMM) and Universal Background Model (UBM) to combine different functions from different resources to implement the SVM based.

Keywords: speaker verification, text-independent, support vector machine, Gaussian mixture model, cepstral analysis

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Authors: Engin Eyceyurt, Josko Zec

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The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: drive test, LTE, machine learning, uplink throughput prediction

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Authors: Nithya Devi

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Poverty, hunger and water scarcity are threats to human rights and are assaults on human dignity. The very existence of man is questioned when his basic rights are violated. Addressing this social phenomenon should be a key objective of any human rights discourse. The origins of these problems have various root causes. For Asia, colonisation was an essential factor that caused great inequalities in the distribution of wealth. In the post-colonial era, the colonised states were developing nations grappling with these issues. Today, some of the developing states have progressed to developed nations. However, others remain as economically vulnerable countries. Within states, the widening income gap poses further threat to human rights. Hence ASEAN states have prioritised socio-economic rights, particularly basic needs, in the human rights discourse in this region. To date, poverty and development goals are given primary importance. This paper seeks to show how a human rights approach has dealt with poverty and development goals in this region and evaluates its effectiveness in addressing these concerns.

Keywords: ASEAN, development, human rights, poverty

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Eyhab El-Kharashi, Maher El-Dessouki

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The paper is dedicated to precise assessment of the induction motor output energy during the unbalanced operation. Since many years ago and until now the voltage complex unbalance factor (CVUF) is used only to assess the output energy of the induction motor while this output energy for asymmetry supply voltage does not depend on the value of unbalanced voltage only but also on the machine parameters. The paper illustrates the variation of the two unbalance factors, complex voltage unbalance factor (CVUF) and impedance unbalance factor (IUF), with positive sequence voltage component, reveals that degree and manner of unbalance in supply voltage. From this point of view the paper delineates the current unbalance factor (CUF) to exactly reflect the output energy during unbalanced operation. The paper proceeds to illustrate the importance of using this factor in the multi-machine system to precise prediction of the output energy during the unbalanced operation. The use of the proposed unbalance factor (CUF) avoids the accumulation of the error due to more than one machine in the system which is expected if only the complex voltage unbalance factor (CVUF) is used.

Keywords: induction motor, electromagnetic torque, voltage unbalance, energy conversion

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Authors: Muhammad Ilham Agus Salim

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This study aims to reconstruct the discourse of human rights which focused on the issue of freedom of religion/belief (FORB) in Indonesia. This topic always has an appeal considering the development of Islam, both as a phenomenon of religion as well as social and political phenomenon, always in touch with human rights issues. For the majority, Islam is involved in human rights discourse needs to be viewed as a natural thing as it also occurs in the majority group in other countries. The natural state is increasingly gaining affirmation when also considering the doctrine of Islam which is also related to human rights. So the involvement of Islamic parties to human rights talks in Indonesia is not as excessive when considering the sociological position and character of Islamic doctrine. But because of who made the object of conversation, namely human rights and particularly freedom of religion or belief again, not something that is taken for granted, then the diversity within Islam itself impossible can be avoided. In this study the diversity of views presented in the trial which categorically can be grouped into two views, namely: inclusive and exclusive.

Keywords: Islam doctrine, Islamic parties, human rights, freedom of religion

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Authors: Kimberly McCabe

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The trafficking of women and children for abuse and exploitation is not a new problem under the umbrella of human trafficking; however, over the last decade, the problem has attracted increased attention from international governments and non-profits attempting to reduce victimization and provide services for survivors. Research on human trafficking suggests that the trafficking of human beings is, largely, a symptom of poverty. As the trafficking of human beings may be viewed as a response to the demand for people for various forms of exploitation, a product of poverty, and a consequence of the subordinate positions of women and children in society, it reaches beyond randomized victimization. Hence, human trafficking, and especially the trafficking of women and children, goes beyond the realm of poorness. Therefore, to begin to understand the reasons for the existence of human trafficking, one must identify and consider not only the immediate causes but also those underlying structural determinants that facilitate this form of victimization. Specifically, one must acknowledge the economic, social, and cultural factors that support human trafficking. This research attempts to study human trafficking at the country level by focusing on economic, social, and cultural characteristics. This study focuses on inequality and, in particular, gender inequality as related to legislative attempts to address human trafficking. Within the design of this project is the use of the US State Department’s tier classification system for Trafficking in Persons (TIP) and the USA CIA Fact Sheet of country characteristics for over 150 countries in an attempt to model legal outcomes as related to human trafficking. Results of this research demonstrate the significance of characteristics beyond poverty as related to country-level responses to human trafficking.

Keywords: child trafficking, gender inequality, human trafficking, inequality

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Authors: Hadi Aghazadeh, Mohammadreza Naeimi, Seyed Ebrahim Afjei, Alireza Siadatan

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Torque ripple, maximum torque and high efficiency are important issues in synchronous reluctance machine (SynRM). This paper presents a view on design of a high efficiency, low torque ripple and high torque density SynRM. To achieve this goal SynRM parameters is calculated (such as insulation ratios in the d-and q-axes and the rotor slot pitch), while the torque ripple can be minimized by determining the best rotor slot pitch in the d-axis. The presented analytical-finite element method (FEM) approach gives the optimum distribution of air gap and iron portion for the maximizing torque density with minimum torque ripple.

Keywords: torque ripple, efficiency, insulation ratio, FEM, synchronous reluctance machine (SynRM), induction motor (IM)

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Ni Komang Desy Arya Pinatih

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Transnational crime is a crime with 'unique' feature because the activities benefit the lack of state monitoring on the borders so dealing with it cannot be based on conventional engagement but also need joint operation with other countries. On the other hand with the flow of globalization and the growth of information technology and transportation, states become more vulnerable to transnational crime threats especially human trafficking. This paper would examine transnational crime activities, especially human trafficking in Indonesia. With the case study on the mapping of human trafficking crime in East Java province, Indonesia, this paper would try to analyze how the difference in human trafficking crime trends at the national and sub-national levels. The findings of this research were first, there is difference in human trafficking crime trends whereas at the national level the trend is rising, while at sub-national (province) level the trend is declining. Second, regarding the decline of human trafficking number, it’s interesting to see how the method to decrease human trafficking crime in East Jawa Province in order to reduce transnational crime accounts in the region. These things are hopefully becoming a model for transnational crimes engagement in other regions to reduce human trafficking numbers as much as possible.

Keywords: transnational crime, human trafficking, southeast Asia, anticipation model on transnational crimes

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

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Authors: Shah Krupa Rajendra, Amit Kumar

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Increasing use of traditional vehicles driven by internal combustion engine is responsible for the environmental pollution. Further, it leads to depletion of limited energy resources. Therefore, it is required to explore alternative energy sources for the transportation. The promising solution is to use electric vehicle. However, it suffers from limited driving range. Regenerative braking increases the range of the electric vehicle to a certain extent. In this paper, a novel methodology utilizing regenerative braking is described. The model comprising of DC machine, feedback based boost converter and micro-controller is proposed. The suggested method is very simple and reliable. The proposed model successfully shows the energy being saved into during regenerative braking process.

Keywords: boost converter, DC machine, electric vehicle, micro-controller, regenerative braking

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Authors: Dawoon Choi, Jian Li, Yunhyun Cho

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Axial Flux Permanent Magnet (AFPM) machines have been widely used in various applications due to their important merits, such as compact structure, high efficiency and high torque density. This paper presents one of the most important characteristics in the design process of the AFPM device, which is a recent issue. To design AFPM machine, the predicting electromagnetic forces between the permanent magnets and stator is important. Because of the magnitude of electromagnetic force affects many characteristics such as machine size, noise, vibration, and quality of output power. Theoretically, this force is canceled by the equilibrium of force when it is in the middle of the gap, but it is inevitable to deviate due to manufacturing problems in actual machine. Such as large scale wind generator, because of the huge attractive force between rotor and stator disks, this is more serious in getting large power applications such as large. This paper represents the characteristics of Double-Stator Inner –Rotor AFPM machines when it has rotor eccentricity. And, unbalanced air-gap and inclined air-gap condition which is caused by rotor offset and tilt in a double-stator single inner-rotor AFPM machine are each studied in electromagnetic and mechanical aspects. The output voltage and cogging torque under un-normal air-gap condition of AF machines are firstly calculated using a combined analytical and numerical methods, followed by a structure analysis to study the effect to mechanical stress, deformation and bending forces on bearings. Results and conclusions given in this paper are instructive for the successful development of AFPM machines.

Keywords: axial flux permanent magnet machine, inclined air gap, unbalanced air gap, rotor eccentricity

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Authors: Adel Atta Youssef Rezkalla

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Russia's invasion of Ukraine tested the international community and prompted not only states but also non-state actors to take deterrent measures in response. In fact, international sports federations, notably FIFA and UEFA, have managed to shift the power dynamic quite effectively by imposing a blanket ban on Russian national teams and clubs. The purpose of this article is to examine the human rights consequences of such actions by international sports organizations. First, the article moves away from assessing the legal status of FIFA and UEFA under international law and examines the question of how a legal connection can be established with their human rights obligations. Secondly, the human rights aspects of the controversial FIFA and UEFA measures against Russian athletes are examined and these are analyzed in more detail using the proportionality test than the principle of non-discrimination under international human rights law. Finally, the main avenues for redress for possible human rights violations related to the actions taken by these organizations are identified and the challenges of arbitration and litigation in Switzerland are highlighted.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security.

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Authors: Neerav Kumar, Naumaan Nayyar, Sharath Kashyap

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Most companies see an increase in the adoption of machine learning (ML) applications across internal and external-facing use cases. ML applications vend output either in batch or real-time patterns. A complete batch ML pipeline architecture comprises data sourcing, feature engineering, model training, model deployment, model output vending into a data store for downstream application. Due to unclear role expectations, we have observed that scientists specializing in building and optimizing models are investing significant efforts into building the other components of the architecture, which we do not believe is the best use of scientists’ bandwidth. We propose a system architecture created using AWS services that bring industry best practices to managing the workflow and simplifies the process of model deployment and end-to-end data integration for an ML application. This narrows down the scope of scientists’ work to model building and refinement while specialized data engineers take over the deployment, pipeline orchestration, data quality, data permission system, etc. The pipeline infrastructure is built and deployed as code (using terraform, cdk, cloudformation, etc.) which makes it easy to replicate and/or extend the architecture to other models that are used in an organization.

Keywords: data pipeline, machine learning, AWS, architecture, batch machine learning

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Authors: Masahiro Yoneda

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The purpose of this study is to identify human walking vertical force by using FFT power spectrum density from the experimental acceleration data of the human body. An experiment on human walking is carried out on a stationary floor especially paying attention to higher components of dynamic vertical walking force. Based on measured acceleration data of the human lumbar part, not only in-phase component with frequency of 2 fw, 3 fw, but also in-opposite-phase component with frequency of 0.5 fw, 1.5 fw, 2.5 fw where fw is the walking rate is observed. The vertical vibration of pedestrian bridge induced by higher components of human walking vertical force is also discussed in this paper. A full scale measurement for the existing pedestrian bridge with center span length of 33 m is carried out focusing on the resonance phenomenon due to higher components of human walking vertical force. Dynamic response characteristics excited by these vertical higher components of human walking are revealed from the dynamic design viewpoint of pedestrian bridge.

Keywords: simplified method, human walking vertical force, higher component, pedestrian bridge vibration

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Authors: Itamir de Morais Barroca Filho, Cephas A. S. Barreto, Ramon Malaquias, Cezar Miranda Paula de Souza, Arthur Costa Gorgônio, João C. Xavier-Júnior, Mateus Firmino, Fellipe Matheus Costa Barbosa

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Information and Communication Technologies (ICT) in healthcare are crucial for efficiently delivering medical healthcare services to patients. These ICTs are also known as e-health and comprise technologies such as electronic record systems, telemedicine systems, and personalized devices for diagnosis. The focus of e-health is to improve the quality of health information, strengthen national health systems, and ensure accessible, high-quality health care for all. All the data gathered by these technologies make it possible to help clinical staff with automated decisions using machine learning. In this context, we collected patient data, such as heart rate, oxygen saturation (SpO2), blood pressure, respiration, and others. With this data, we were able to develop machine learning models for patients’ risk of death and estimate the length of stay in ICU beds. Thus, this paper presents the methodology for applying machine learning techniques to develop these models. As a result, although we implemented these models on an IoT healthcare platform, helping clinical staff in healthcare in an ICU, it is essential to create a robust clinical validation process and monitoring of the proposed models.

Keywords: ICT, e-health, machine learning, ICU, healthcare

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Authors: Dimple Juneja, Aarti Singh, Renu Hooda

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Knowledge Query Manipulation Language (KQML) and FIPA ACL are two prime communication languages existing in multi agent systems (MAS). Both languages are more or less similar in terms of semantics (based on speech act theory) and offer cutting edge competition while establishing agent communication across Internet. In contrast to the fact that software agents operating on the internet are required to be more safeguarded from their counter-peer, both protocols lack security performatives. The paper proposes a three tier security interface with few novel security related performatives enhancing the basic architecture of KQML. The three levels are attestation, certification and trust establishment which enforces a tight security and hence reduces the security breeches.

Keywords: multiagent systems, KQML, FIPA ACL, performatives

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Authors: Reyhan Ozbask, Emek Moroydor Derin, Mustafa Dogu

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There are a limited number of global companies in the world that manufacture and commercially offer thermoplastic composite prepregs in accordance with aerospace requirements. High-performance thermoplastic materials supplied for aerospace structural applications are PEEK (polyetheretherketone), PPS (polyphenylsulfite), PEI (polyetherimide), and PEKK (polyetherketoneketone). Among these, PEEK is the raw material used in the first applications and has started to become widespread. However, the use of these thermoplastic raw materials in composite production is very difficult due to their high processing temperatures and impregnation difficulties. This study, it is aimed to develop carbon fiber-reinforced thermoplastic PEEK composites that comply with the requirements of the aviation industry that are superior mechanical properties as well as being lightweight. Therefore, it is aimed to obtain high-performance thermoplastic composite materials with improved interface properties by using the sizing method (suspension development through chemical synthesis and functionalization), to optimize the production process. The use of boron nitride nanotube as a bonding agent by modifying its surface constitutes the original aspect of the study as it has not been used in composite production with high-performance thermoplastic materials yet. For this purpose, laboratory-scale studies on the application of thermoplastic compatible sizing will be carried out in order to increase the fiber-matrix interfacial adhesion. The method respectively consists of the selection of appropriate sizing type, laboratory-scale carbon fiber (CF) / poly ether ether ketone (PEEK) polymer interface enhancement studies, manufacturing of laboratory-scale BNNT coated CF/PEEK woven prepreg composites and their tests.

Keywords: carbon fiber reinforced composite, interface enhancement, boron nitride nanotube, thermoplastic composite

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Authors: Ioannis Andrianakis, Vasileios Gkatas, Nikos Eleftheriadis, Alexios Ellinidis, Ermioni Avramidou

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pscmsForecasting is an open-source web service that implements a variety of time series forecasting algorithms and exposes them to the user via the ubiquitous HTTP protocol. It allows developers to enhance their applications by adding time series forecasting functionalities through an intuitive and easy-to-use interface. This paper provides some background on time series forecasting and gives details about the implemented algorithms, aiming to enhance the end user’s understanding of the underlying methods before incorporating them into their applications. A detailed description of the web service’s interface and its various parameterizations is also provided. Being an open-source project, pcsmsForecasting can also be easily modified and tailored to the specific needs of each application.

Keywords: time series, forecasting, web service, open source

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Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

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Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: moving object detection, histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine

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Authors: Nhabhat Chaimongkol

Abstract:

Human capital is one of the factors that are vital for economic growth. This is especially true as humans will face increasingly more forms of disruptive technology in the near future. Therefore, there is a need to develop human capital in order to overcome the current uncertainty in the global economy and the future of jobs. In recent years, Thailand has increasingly devoted more attention to developing its human capital. The Thai government has raised this issue in its national agenda, which is part of its 20-year national strategy. Currently, there are multiple challenges and solutions regarding this issue. This study aims to find out what are the challenges and solutions to human capital development in Thailand. The research in this study uses mixed methods consisting of quantitative and qualitative research methods. The results show that while Thailand has many plans to develop human capital, it is still lacking the necessary actions and integrations that are required to achieve its goals. Finally, the challenges and solutions will be discussed in detail.

Keywords: challenges, human capital, solutions, Thailand

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