Existence, uniqueness and stability results of impulsive stochastic semilinear neutral functional differential equations with infinite delays

This article presents the results on existence, uniqueness and stability of mild solutions of impulsive stochastic semilinear neutral functional differential equations without a Lipschitz condition and with a Lipschitz condition. The results are obtained by using the method of successive approximations

Non-linear control algorithms for an unmanned surface vehicle

Although intrinsically marine craft are known to exhibit non-linear dynamic characteristics, modern marine autopilot system designs continue to be developed based on both linear and non-linear control approaches. This article evaluates two novel non-linear autopilot designs based on non-linear local control network and non-linear model predictive control approaches to establish their effectiveness in terms of control activity expenditure, power consumption and mission duration length under similar operating conditions. From practical point of view, autopilot with less energy consumption would in reality provide the battery-powered vehicle with longer mission duration. The autopilot systems are used to control the non-linear yaw dynamics of an unmanned surface vehicle named Springer. The yaw dynamics of the vehicle being modelled using a multi-layer perceptron-type neural network. Simulation results showed that the autopilot based on local control network method performed better for Springer. Furthermore, on the whole, the local control network methodology can be regarded as a plausible paradigm for marine control system design. © 2014 IMechE

A Comparison of Supervised Learning Techniques for Predicting the Mortality of Patients with Altered State of Consciousness

The study attempts to identify a potentially reliable supervised learning technique for predicting the outcomes of mortality in an altered state of consciousness (ASC) patients. ASC is a state distinguished from ordinary waking consciousness, which is a common phenomenon in the Emergency Department (ED). Thirty (30) distinctive attributes or features are commonly used to recognize ASC. The study accordingly applied these features to model the prediction of mortality in ASC patients. Supervised learning techniques are found to be suitable for such classification problems. Consequently, the study compared five supervised learning techniques that are commonly applied to evaluate the risk of mortality using health-related datasets, namely Decision Tree, Neural Network, Random Forest, Naïve Bayes, and Logistic Regression. The labeled dataset comprised patient records captured by the Universiti Sains Malaysia hospital’s Emergency Medicine department from June to November 2008. The cleaned dataset was divided into two parts. The larger part was used for training and the smaller part, for evaluation. Since the ratio between training and testing samples varies between individual supervised learning techniques, we studied the performance of the modeled techniques by also varying the proportion of the training data to the dataset. We applied four percentage splits; 66%, 75%, 80%, and 90% to allow for 3-, 4-, 5- and 10-fold cross-validation experiments to evaluate the accuracy of the analyzed techniques. The variation helped to lessen the chance of over fitting, and averaged the effects of various conditions on accuracy. The experiments were conducted in the WEKA environment. The results indicated that Random Forest is the most reliable technique to model for predicting the mortality in ASC patients with acceptable accuracy, sensitivity, and specificity of 70.9%, 76.3%, and 65.5%, respectively. The results are further confirmed by SROC analysis. The findings of the study serve as a fundamental step towards a comprehensive study in the future

Measurement Of Film Thickness And Temperature On Horizontal Metal Spray Coated Tube Falling Film Evaporator Using Interferometric Techique

’Water’ is the ’Essence of Life’. It is an irreplaceable precious resource that is core of life on earth, a vital commodity that is critical for human survival, socio-economic developments and for the preservation of a healthy ecosystem. Current trends indicate that two-thirds of the world’s population will be living in water-stressed countries by 2025(wat (2006)). In order to eradicate or to provide sufficient water requirement for mankind desalination plays a pivotal role. This paper presents studies on horizontal tube falling film evaporator for Multi effect desalination (MED) system with spray coated tubes. The most important component in any MED system is the falling film evaporator. The wide acceptance for this kind of evaporators is because of the fact that it is characterized by a very low-pressure drop. In MED systems, falling film evaporation takes place outside the tube geometry utilizing the latent heat of condensing vapour inside the tube. Convective evaporation, as well as low-temperature nucleate boiling, occur in the film as it flows over the tube depending on the operating conditions(Abraham and Mani (2015)). The liquid falls on the top of the tube and flows down along the curved tube surface. There is a phase change on both sides of the tube and the evaporation outside the tube helps vapour to be separated from the liquid as soon as it is formed. Two different tubes surfaces were studied, namely bare copper tube and copper tube coated with alloy of Al2O3 and TiO2. Scanning electron microscope, Energy-dispersive X-ray spectroscopy, 3D surface profilometer were utilized to study about the surface texture, composition and to find surface roughness values attributed with each tubes. An optical shadow method (non-intrusive) incorporating Otsus’s algorithm was used to evaluate the film thickness around the circumference of the cylinder, and a Mach-Zehnder Interferometer (MZI) was employed to visualize the isotherm formation (Maliackal et al. (2021)). All studies were performed for complete wetting of the tube. The measured film thickness was compared with commonly used empirical formulas. Further, the effectiveness of using those empirical formulas for small diameter tubes was analyzed. A novel interferometric technique was used to analyze the film interface temperature, and a comparative study was performed for the two different tube geometries. A standard error of mean (SEM) analysis was performed on every data set

Using And Interacting On An Online Narrative Writing Platform: An Exploratory Study

The advent and the continued development of the Internet has given birth to a plethora of online writing apps, softwares and platforms that provide an opportunity for educators and students alike to develop and enhance their writing skill online. The main aim of this exploratory study is to investigate how the use of an online narrative writing platform as defined in this study enhances students’ narrative writing. The study also investigates the patterns of interactions in a collaborative learning environment and the participants’ experiences and reflections in the use of the online narrative writing platform. The study explores the use of the online narrative writing platform in a specific setting of six students and a teacher in a Chinese Secondary school in Penang. The data sources of this qualitative exploratory study are written assignments, online interactions, interviews and reflections. Qualitative data analysis procedures include stages of data preparation and organisation, data reduction into themes through coding processes and final representation of findings. The multiple sources of data enabled data triangulation process to be carried out. The findings revealed that the students improved their narrative writing ability after engaging in the online narrative writing platform. The patterns of interactions were analysed according to cognitive, teaching and social presences. The social presence showed the teacher and the students were constantly acknowledging and caring for one another. For teaching presence, the teacher played a major role as the subject matter expert and in directing the students’ attention to important aspects of the task. The cognitive presence, showed that students and teacher are giving and receiving comments to contribute to thinking and learning. An additional presence related to social presence was identified. This is termed as social learning presence

Teleoperation control of Baxter robot using Kalman filter-based sensor fusion

Kalman filter has been successfully applied to fuse the motion capture data collected from Kinect sensor and a pair of MYO armbands to teleoperate a robot. A new strategy utilizing the vector approach has been developed to accomplish a specific motion capture task. The arm motion of the operator is captured by a Kinect sensor and programmed with Processing software. Two MYO armbands with the inertial measurement unit embedded are worn on the operator's arm, which is used to detect the upper arm motion of the human operator. This is utilized to recognize and to calculate the precise speed of the physical motion of the operator's arm. User Datagram Protocol is employed to send the human movement to a simulated Baxter robot arm for teleoperation. In order to obtain joint angles for human limb utilizing vector approach, RosPy and Python script programming has been utilized. A series of experiments have been conducted to test the performance of the proposed technique, which provides the basis for the teleoperation of simulated Baxter robot

Development of writing task recombination technology based on DMP segmentation via verbal command for Baxter robot

This paper developed a character recombination technology based on dynamic movement primitive (DMP) segmentation using verbal command on a Baxter robot platform. Movements are recorded from a human demonstrator. The operator physically guides the Baxter robot to perform the movements for five times. This training data set is also utilized for playback process. Subsequently, the dynamic time warping is employed to pre-treat the data. The DMP is used to model and generalize every single movement. Gaussian mixture model is used to generate multiple patterns after the teaching process. Then the Gaussian mixture regression algorithm is applied to reduce the position errors in 3D space after the generation of a synthesized trajectory. A remote PC is used to control the command of Baxter to record or playback any trajectories via user datagram protocol (UDP) by typing commands in a text file. In addition, Dragon NaturalSpeaking software is used to transfer the voice data to text data. This proposed approach is tested by performing a Chinese character writing task with a Baxter robot, where different Chinese characters are written by teaching only one character

Pool inference attacks on local differential privacy: quantifying the privacy guarantees of apple's count mean sketch in practice

Behavioral data generated by users’ devices, ranging from emoji use to pages visited, are collected at scale to improve apps and services. These data, however, contain fine-grained records and can reveal sensitive information about individual users. Local differential privacy has been used by companies as a solution to collect data from users while preserving privacy. We here first introduce pool inference attacks, where an adversary has access to a user’s obfuscated data, defines pools of objects, and exploits the user’s polarized behavior in multiple data collections to infer the user’s preferred pool. Second, we instantiate this attack against Count Mean Sketch, a local differential privacy mechanism proposed by Apple and deployed in iOS and Mac OS devices, using a Bayesian model. Using Apple’s parameters for the privacy loss ε, we then consider two specific attacks: one in the emojis setting — where an adversary aims at inferring a user’s preferred skin tone for emojis — and one against visited websites — where an adversary wants to learn the political orientation of a user from the news websites they visit. In both cases, we show the attack to be much more effective than a random guess when the adversary collects enough data. We find that users with high polarization and relevant interest are significantly more vulnerable, and we show that our attack is well-calibrated, allowing the adversary to target such vulnerable users. We finally validate our results for the emojis setting using user data from Twitter. Taken together, our results show that pool inference attacks are a concern for data protected by local differential privacy mechanisms with a large ε, emphasizing the need for additional technical safeguards and the need for more research on how to apply local differential privacy for multiple collections