Miracle-dz/UniFAR-FTUs huggingface dataset:
Communication Delay and Control in Telerobotics

presented by the block diagram of Fig. I and con sists of five subsystems : the buman operator, the master, the communication block, the slave, and the environment. The operator commands a ve locity forward, through the master, communica tion block, and slave, to the environment. Pro viding contact force information to the operator by feeding back force signals form the slave to the master is an important way to improve task per formance. When this is done, the contact force is said to be "reflected" to the operator, and the teleoperator is said to be controlled bilaterally. Ideally, the slave is controlled so that in the steady state, the slave velocity ƒÒs is equal to the operator velocity ƒÒm and the backdriven force Fmd is equal to the contact force Fe. Because the slave is controlled remotely, a time delay is incurred in the transmission of velocity and force signals between the master and slave manipulators. These time delays are caused by computation delay, network protocols, and trans mission of signals over large distances such as in undersea and near earth orbit operations. It has long been known that these time delays can desta bilize a bilaterally controlled teleoperator6). In this paper we describe some recent work based on Network and Passivity Theory that compensates for the time-delay in bilateral teleoperator systems. Mainly we will discuss the results in the references3),12) with some additional comments and results based on more recent extensions to this work by several authors, for example the reference1),11). The paper is organized as follows. In section 2 we give some mathematical background in Network Theory and Passivity. Section 3 shows how thee instability due to time delay arises using Scat tering Theory. In section 4 we discuss the ap proach of Anderson and Spong3) which uses the Scattering Theory formalism to compensate for the time-delays in communication. In section 5 we discuss an alternative formulation of the method from the reference3), due to Niemeyer and Slo tine12), which uses the idea of power flow and wave variables. In section 6 we discuss the use of impedance matching to improve the transient performance of the system. Finally we give some conclusions in section 7.
Application of artificial neural networks to stylometric analysis

Stylometric analysis of a literary text can comprise several elements and its aims include authorship attribution, author characterisation and similarity detection, which fall within the scope of information retrieval tasks. Author identification is a problem of significant importance not only from academic or historic point of view as in cases of disputed authorship of some literary works (even Shakespeare's) but in more current and sinister affairs of forensic nature. To deal with these problems various methodologies have been employed or invented, and in the paper there is presented the application of Artificial Neural Networks as the processing engine for authorship analysis.
Time Constrained Data Destruction in Cloud

This paper presents some of the important details of the client is stored in the cloud. Personal data that can be stored in the cloud and other important information that could be used by other user. These data are cached, copied by the cloud service providers. The security for the content stored in the cloud is very important. For that the system used in this to give the security in the cloud. The service provider owns the equipment and is responsible for housing, running and maintaining it. The data stored in the cloud is destructed automatically when the time limit exceeded. RSA is used for the data encryption and decryption. And also the time specification is also specified by the client. The data is deleted from storage node without interruption of the user. It is used in the time of uploading and downloading the data in the cloud. MAC is used to check the integrity of the data stored in the storage nodes.
Next-Generation 3D Printed Microfluidic Membraneless Enzymatic Biofuel Cell: Cost-Effective and Rapid Approach

3D printing offers a novel, time-efficient, and multi-material fabrication platform for numerous applications owing to its potential to rapidly manufacture low-cost and 3D printed (3DP) structures. In this paper, polylactic acid (PLA) and conductive composite graphene/PLA filaments were used to fabricate 3DP microchannels and electrodes intended for enzymatic biofuel cells (EBFCs) using a commercial bench-top 3D printer. This 3D printing technology delivers a simplistic, cost-effective, and quick fabrication process, which eliminates the requirement of any further amendment and post-processing. With this technology, we demonstrate structurally simple, miniature 3DP microfluidic membraneless EBFCs (3DP-MM-EBFCs), served with glucose and oxygen with the utilization of redox mediators generating continuous power. The cell performance was evaluated at different flow rates, obtaining open circuit potential (OCP) 0.425 V and maximum peak power density (PD) <inline-formula> <tex-math notation="LaTeX">$4.15~\mu \text{W}$ </tex-math></inline-formula>/cm<sup>2</sup> at a current density (CD) of <inline-formula> <tex-math notation="LaTeX">$13.36~\mu \text{A}$ </tex-math></inline-formula>/cm<sup>2</sup>. The advancement in 3DP electrode surface characteristic and the dimethylformamide (DMF) treatment is carried onto the electrode surface followed by the immobilization of the enzymes individually. The 3DP fabrication technology demonstrated the feasibility of simple and advanced microfabrication techniques to build a well-organized plug-and-play and rapidly analyzed device to power several portable low-power devices.
Optimization of Education Management System Based on Web

With the advent of the global information age and the development of computer science, the positive role of information management technology in social life becomes more and more obvious. Computer-aided management has gone from scratch. The course of development is from immaturity to maturity. By referring to the experience of computer-aided teaching management in many foreign countries, education computer-aided management in our country has developed gradually. In terms of schools, the teaching management is a very important but very troublesome thing, if with the combination of information technology and teaching management information system, the efficiency of the school's teaching management can be greatly improved to reduce the data statistics and analysis of data. Teaching management system development method for top-down and modular system development method includes the development and the order for the system analysis, system design, system implementation, system summary and detailed design and realization of online inquiry, and the administrator to realize modify functions, schedule arrangement, login result, etc. of students’ information. The system is divided into front desk and back desk. The front desk is direct to the user, conducting various inquiries about teaching; The background is data statistics, management and control.
Numerical and Experimental Determination of Canard Controlled Missile Aerodynamic Coefficients in Subsonic Regime

This paper discusses the computational and experimental approach to the determination of aerodynamic coefficients of canard controlled missile with wrap around tail fin. The paper presents results of subsonic flow at Mach 0.4. Angle of attack of the missile is varied from −10 to +10 degrees with an increment of 2 degrees. Canard deflection in the pitch plane is also varied at 0, 10, and 15 degrees. The results of the simulation are compared with the experimental data obtained by the wind tunnel measurements. Measured results confirm applicability of Spalart Allmaras turbulence model with adapted computational mesh to the determination of aerodynamic coefficients for canard controlled missiles.
The State of Computer Simulation Applications in Construction

Construction operations are performed under different working conditions including (but not limited to) unexpected weather conditions, equipment breakdown, delays in procurement, etc. As such, computer simulation is considered an appropriate technique for modeling the randomness of construction operations. Several construction processes and operations have been modeled utilizing computer simulation such as earthmoving, construction of bridges and tunnels, concrete placement operations, paving processes, and coordination of cranes operations. This chapter presents an overview of computer simulation efforts that have been performed in the area of construction engineering and management. Also, it presents two computer simulation applications in construction; earthmoving and construction of bridges’ decks. Comprehensive case studies are worked out to illustrate the practicality of using computer simulation in scheduling construction projects, taking into account the associated uncertainties inherited in construction operations.
Packet header compression for the internet of things

Due to the extensive growth of Internet of Things (IoT), the number of wireless devices connected to the Internet is forecasted to grow to 26 billion units installed in 2020. This will challenge both the energy efficiency of wireless battery powered devices and the bandwidth of wireless networks. One solution for both challenges could be to utilize packet header compression. This paper reviews different packet compression, and especially packet header compression, methods and studies the performance of Robust Header Compression (ROHC) in low speed radio networks such as XBEE, and in high speed radio networks such as LTE and WLAN. In all networks, the compressing and decompressing processing causes extra delay and power consumption, but in low speed networks, energy can still be saved due to the shorter transmission time. Copyright © 2016 IFSA Publishing, S. L.
Resource management for dynamic, distributed real-time systems

A current challenge facing resource management is the need to deploy real-time systems in dynamic operational environments. The systems are often affected by unpredictable environmental factors that cannot be known a priori and have no meaningful worst-case estimates. As a result, traditional resource allocation techniques do not apply. Current attempts to address these systems have been limited. This research addresses the resource management problem for dynamic, distributed real-time systems (DDRTSs). A resource allocation approach is developed for these systems that offers explicit real-time guarantees with maximized tolerance (robustness) of unpredictable environment changes. This work has developed (1) a real-time computing model that incorporates environmental factors, (2) metrics that characterize robustness, and (3) resource allocation algorithms that find feasible, robust allocations. The approach is validated by both theoretical analysis and experimentation. The main contribution of the work is a reliable resource management solution for DDRTSs, which allows real-time applications to be versatile in real world environments.
Optimized Model and Algorithm of Motor Train-sets Scheduling for Dedicated Passenger Lines

On the premise in unfixed train-set used sections,with circular permutation of all trains as train-set routing and time and mileage intervals as the constraints of daily maintenance and primary maintenance in circular permutation,the optimized model integrating operation planning and maintenance planning of train-sets is built to minimize the connecting costs of trains.The routing of train-sets in circular permutation effectively describes the uniformity of train-set operation,and the constraints of daily maintenance and primary maintenance make the train-sets maintenance plans possible to be included into the train-sets operation plans for optimization.The simulated annealing algorithm is designed to solve the model by introduction of the penalty function and 3-opt neighborhood structure.Finally operation planning and maintenance planning of 144 trains are calculated and the optimizing indexes are evaluated.
Tackling Uncertainty for the Development of Efficient Decision Support System in Air Traffic Management

Airport capacity has become a constraint in the air transportation networks due to the growth of air traffic demand and the lack of resources able to accommodate this demand. This paper presents the algorithmic implementations of a decision support system for making a more efficient use of the airspace and ground capacity. The system would be able to provide support for air traffic controllers in handling large amount of flights while reducing to a minimum potential conflicts. In this framework, airspace together with ground airport operations is considered. The conflicts are defined as separation minima violation between aircraft for what concerns airspace and runways and as capacity overloads for taxiway network and terminals. The methodology proposed in this paper consists of an iterative approach that couples optimization and simulation to find solutions that are resilient to perturbations due to the uncertainty present in different phases of the arrival and departure process. An optimization model was employed to find a (sub)optimal solution, while a discrete event-based simulation model evaluated the objective function. By coupling simulation with optimization, we generate more robust solutions resilient to variability in the operations, and this is supported by a case study of Paris Charles de Gaulle Airport.
A Novel Application of Deep Belief Networks in Learning Partial Discharge Patterns for Classifying Corona, Surface, and Internal Discharges

This paper introduces a new application of deep belief network (DBN) as an emerging artificial neural network for recognizing and classifying different partial discharge (PD) patterns. Phase resolved PD (PRPD) technique with different window intervals is used to manipulate three PD types, including corona, surface, and internal discharges measured in a high-voltage lab. Four approaches are proposed for extracting features from the raw measured data. In the first approach, the DBN is used as both a feature extractor and a PD classifier. The other three approaches extract discriminatory features using statistical and vector-norm-based operators to train a DBN classifier. The impact of the various phase windows through the PRPD method on the performance of the trained classifier is evaluated to obtain the best window interval. It is shown that the deep architectures are capable of learning important distinguishable features from PD data without any data preprocessing. This eliminates time-consuming feature extraction processes that produce the handcrafted features. Based on a comparison analysis, when the input data are corrupted by noise levels or no feature extraction technique is used to preprocess the data, the proposed approach outperforms other techniques, such as artificial neural networks, fuzzy logic classifiers, and support vector machines.
Automatic Detection and Staging of Lung Tumors using Locational Features and Double-Staged Classifications

Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.
Retention of Employees in Ceramic Sanitary Ware Factories in India: Role of Work Life Balance, Career Development and Supervisor Support

The study examines the role of work life balance, career development and supervisor support on organization commitment over employees of unattended, ceramic sanitary ware factories in India. It also verifies the influence of organization commitment on retention and its mediating role. Findings reveal that organization commitment influences retention and all the above factors enhance it. Moreover, organization commitment partially mediates the relationship between proposed factors and retention. It also found that organization’s career development provision alone is not enough and need to be modified according to the employer’s expectation. Managerial implications and suggestions for future research were discussed.
LI Cancellation and Power Allocation for Multipair FD Relay Systems With Massive Antenna Arrays

Massive antenna arrays are capable of canceling out the loop interference (LI) at the relay station in multipair full-duplex (FD) relay networks even without LI channel knowledge if the number of antennas is allowed to grow without a bound. For large but finite number of antennas, however, channel estimation-based LI cancelation is required. In this letter, we propose a pilot protocol for LI channel estimation by exploiting the channel coherence time difference between static and moving transceivers in a multipair FD relay system. To maximize the end-to-end achievable rate, we also design a novel power allocation scheme to adjust the transmit power of each link at the relay. The analytical and numerical results show that the proposed novel pilot protocol and power allocation scheme jointly improve spectral and energy efficiency significantly with realistic coherence time differences.
Performance Measurement Using Balance Score Card and its Applications:A Review

This paper aims to propose the idea of briefly explaining the balance scorecard by highlighting its use, application in depth. A critical enabler in achieving desired performance goals is the ability to measure performance. Despite the importance of accurately measuring organisational performance in most areas of academic research, there have been very few studies that have directly addressed the question of how overall organisational performance is or should be measured. Perhaps more importantly, none of these studies seems to have significantly influenced how overall organisational performance is actually measured in most of the empirical research that uses this construct as a dependent measure. The most popular of the performance measurement framework has been the balanced scorecard abbreviated as BSC. The BSC is widely acknowledged to have moved beyond the original ideology. It has now become a strategic change management and performance management process. The approach used in this paper is the combination of literature review on evolution of balance score card and its applications in various sectors/organisations/areas. This paper identify that the balanced scorecard is a powerful but simple strategic tool and the simplicity of the scorecard is in its design. By encompassing four primary perspectives, the tool allows an organisation to turn its attention to external concerns, such as the financial outcomes and its customers' expectations, and internal areas, which include its internal processes to meet external requirements and its integration of learning and growth, to successfully meet its strategic expectations. This paper provides a comprehensive overview of the balanced scorecard combined with application and strategy, which are now in a better position to begin to recognise management's expectations and to discover new ways to build value for workplace learning and performance within organisation.
Explore Knowledge-Sharing Strategy and Evolutionary Mechanism for Integrated Project Team Based on Evolutionary Game Model

Integrated Project Delivery (IPD) has become increasingly popular in the architecture, engineering, and construction industries. However, the current practice status by the construction industry fails to deliver the desired results. In that backdrop, how to promote cooperation within and improve the overall performance of integrated project team has received wide attention. Herein, knowledge-sharing plays a critical role in cooperation and overall performance. However, to the best of our knowledge, the research on knowledge-sharing strategy interaction and evolutionary mechanism is rare. To make up for the deficiency of the studies existing, a novel model is proposed by taking advantage of evolutionary game theory, to capture the interaction behavior of knowledge-sharing and explore its evolutionary mechanism. Six parameters of knowledge stock, knowledge-sharing degree, heterogeneous knowledge proportion, synergy effect, knowledge absorption coefficient, and knowledge-sharing cost efficient that are critical to knowledge-sharing are extracted and defined. The payoff matrix is constructed by analyzing the benefits and costs of knowledge-sharing. Then, a replicator dynamic system is established based on payoff matrix, to determine the evolutionary tendency of knowledge-sharing behavior. Finally, numerical simulations are conducted to explore the influences of all parameters on the knowledge-sharing strategy. The findings in this research reveal that strategy interaction behavior is significantly influenced by proportion of strategy of choosing to share knowledge in both game players. The authors also find that strategy interaction behavior has a strong negative correlation with knowledge-sharing cost efficient, but has a positive correlation with knowledge stock, heterogeneous knowledge proportion, degree of knowledge-sharing, knowledge absorption coefficient, and synergetic effect coefficient. This research can provide the evolutionary mechanism and broaden our understanding of relationship between project performance and knowledge-sharing and can offer valuable guidance on improving cooperation and performance of project teams.
A lever concept integrated with simple rules for flow shop scheduling

The development of more efficient and better performing priority dispatching rules (PDRs) for production scheduling is relevant to modern flow shop scheduling practice because they are simple, easy to apply and have low computational complexity, especially for large-scale problems. While the current research trend in scheduling is towards finding superior solutions through meta-heuristics, they are computationally expensive and many meta-heuristics also use PDRs to generate starting points. In this paper, we analyse the properties of flow shop scheduling problems to minimise maximum completion time, and generate a new dominance rule that is complementary to Szwarc’s rule. These dominance rules indicate that a weighting factor should be included in sequencing to account for the possibility that a single job’s processing time can generate idle time repeatedly within a flow line. Two new PDRs with a leveraged weighting factor are proposed to minimise makespan and average completion time. Computational results on Taillard’s benchmark problems and on historical operating room data show that the proposed PDRs perform much better than established PDRs without an increase in computational complexity.