Miracle-dz/UniFAR-FTUs huggingface dataset:
Software Design Patters in IEC 61131–3 Systems: A Case Study

Software design patterns have matured to be an standard tool in mainstream software engineering. They have however not found their way into the industrial automation domain, where a lot of the software is implemented in IEC 61131–3 languages. This paper builds on an existing body of knowledge to identify software design patterns in two automation system case studies. The patterns, which were identified are discussed and evaluated under the aspects of code quality, reusability and usefulness for documentation.
Semi-Supervised Transfer Learning with Genetic Algorithm Tuned Transformation and Novel Label Transfer Mechanism

Robotics and intelligent sensing methods are experiencing a new wave applications through the use of machine learning systems. Intelligence is being introduced in robots and sensor platforms by utilizing machine learning techniques such as classification. In the field of robotics, generating training data can be very complex and often, expensive. In this set-up, transfer learning can greatly improve the performance of a classifier wherever and whenever enough labeled data is not available in a domain of interest (target domain), but ample labeled data can be found in a different but related domain (source domain). A new optimized method is proposed in this work to transform the observation from source domain along with a new label transfer mechanism. The transformed, or adapted, domain has the same number of features as the target domain and the same number of observations from the source domain. Labels are transferred from source to target domain using a multivariate Gaussian mixture model (GMM). Genetic algorithm is used to optimize the transformation process by minimizing a cost function that addresses both distribution difference and accuracy. Experiments show that the proposed method outperforms any classifier trained only with source or target domain data.
Multi-critic DDPG Method and Double Experience Replay

The remarkable Deep Deterministic Policy Gradient (DDPG) reinforcement learning method commonly consists of actor learning and critic learning. The actor learning highly relies on the critic learning, which makes the performance of DDPG method rather sensitive to critic learning and leads to stability issues. To further improve the stability and performance of DDPG method, the multi-critic DDPG method (MCDDPG) is proposed for a reliable critic learning. The average value of multiple critics is used to replace the single critic in DDPG method for better resistance when one critic performs badly, and multiple independent critics can learn knowledges from environment more widely. Besides, an extension of experience replay mechanism is revealed for accelerating the training process. All the methods are tested on simulated environments in OpenAI Gym platform, and convincing experiment results are obtained to support the proposed methods.
The Impact of Organizational Legitimacy on Product Innovation: A Comparison Between New Ventures and Established Firms

What is the impact of organizational legitimacy (OL) on the firm innovation? The extant literature shows ambiguous answers. This study argues that such an ambiguity stems from two neglected facts. First, OL is audience dependent. Second, OL varies across firm life cycle stages. This study clarifies political legitimacy (PL) and market legitimacy (ML) as two key types of OL, and distinguishes them in resources they provide access to and firm behaviors they result in. Then, it examines the impact of PL and ML on the product innovation in new ventures and established firms, respectively. Drawing on the data of 211 firms, this study finds that the relationship of PL to product innovation is inverted U-shaped in new ventures, while negative in established firms, and the linkage between ML and product innovation is positive in new ventures but inverted U-shaped in established firms. This study not only draws a more comprehensive picture about the impact of OL on the product innovation, but also lays down a threshold over which to elaborate the implications of OL. In addition, it guides new ventures and established firms in taking advantage of OL to foster the product innovation.
Analysis of Population Size in Artificial Bee Colony Algorithm

Artificial bee colony (ABC) algorithm has attracted growing interest for the continuous global optimization problems (CGOPs), where numerous algorithmic extensions have been developed. However, existing studies generally employ identical population size to perform the comparison among different ABC variants, regardless a fact that the generally suitable population size should be algorithm-dependent. Here we focus on the analysis of population size. This study is conducted in several well-known ABC variants under a set of benchmark CGOPs. We demonstrate that i) with the independently optimal population size, standard ABC can perform competitively comparing with its advanced variants, and ii) the most remunerative population size is related to the algorithmic exploitation/exploration ability. We anticipate that this study will provide useful insights to guide the appropriate usage of ABC, as well as its further enhancements.
Automatic Detection of Valves with Disaster Response Robot on Basis of Depth Camera Information

In recent years, there has been an increasing demand for disaster response robots designed for working in disaster sites such as nuclear power plants where accidents have occurred. One of the tasks the robots need to complete at these kinds of sites is turning a valve. In order to employ robots to perform this task at real sites, it is desirable that the robots have autonomy for detecting the valves to be manipulated. In this paper, we propose a method that allows a disaster response robot to detect a valve, whose parameters such as position, orientation and size are unknown, based on information captured by a depth camera mounted on the robot. In our proposed algorithm, first the target valve is detected on the basis of an RGB image captured by the depth camera, and 3D point cloud data including the target is reconstructed by combining the detection result and the depth image. Second, the reconstructed point cloud data is processed to estimate parameters describing the target. Experiments were conducted on a simulator, and the results showed that our method could accurately estimate the parameters with a minimum error of 0.0230 m in position, 0.196 % in radius, and 0.00222 degree in orientation.
INTEGRASI PERTUMBUHAN EKONOMI DAN PENCIPTAAN KESEMPATAN KERJA SEKTOR PERTANIAN DI INDONESIA

Penelitian ini bertujuan menganalisis integrasi pertumbuhan ekonomi dan penciptaan kesempatan kerja sektor pertanian. Hasil analisis bermanfaat bagi pemerintah untuk meninjau apakah kebijakan pembangunan ekonomi yang yang dilaksanakan selama ini telah mendukung penciptaan kesempatan kerja baru di sektor pertanian, apakah sektor pertanian masih mampu menciptakan kesempatan kerja baru serta bagaimanaarah pembangunan ekonomi sektor pertanian ke depan untuk dapat mendukung penciptaan kesempatan kerja baru. Data yang digunakan adalah data sekunder tahun 1977-2012 untuk variabel Produk Domestik Bruto dan kesempatan kerja sektor pertanian. Data dikumpulkan dengan metode dokumentasi. Analisis data menggunakan model Vector Autoregression (VAR). Hasil analisis menunjukkan pertumbuhan kesempatan kerja selalu berada di bawah pertumbuhan ekonomi selama kurun waktu 1977-2012 bersifat labor intensivesehingga tidak mendukung penciptaan kesempatan kerja baru. Pertumbuhan ekonomi dan kesempatan kerja sektor pertanian tidak terintegrasi secara sempurna. Hal ini berarti tenaga kerja yang bekerja di sektor pertanian sudah berlebih dari kapasitas penyerapan sektor pertanian. Penciptaan kesempatan kerja baru di sektor pertanian ke depan hanya dapat diarahkan untuk lapangan kerja yang berkaitan dengan industrialisasi pertanian, namun hal ini harus disertai dengan peningkatan kualifikasi tenaga kerja pertanian agar sesuai dipekerjakan di sektor industri.
Path Prefetching: Accelerating Index Searches for In-Memory Databases

In-memory databases (IMDBs) store all working data in main memory, which makes memory accesses become the dominant factor of the whole system performance. Micro-architectural studies of mainstream in-memory on-line transaction processing (OLTP) systems show that more than half of the execution time goes to memory stalls. Moreover, for IMDBs that adopt aggressive transaction compilation optimizations, data misses from the last-level cache (LLC) are responsible for the majority of the overall stall time. In this paper, through profiling analysis of IMDBs we observe that index access misses dominate LLC data misses. Based on the key observation that adjacent keys tend to follow similar traversal paths in ordered index searches, we propose the path prefetching to mitigate LLC misses induced by ordered index searches, which records mappings between keys and their traversal paths and then generate prefetches for future same/adjacent keys. Experimental results show that for ordered index searches the proposed path prefetcher provides an average speedup of 27.4% over the baseline with no prefetching.
Multi-Classifier Speech Emotion Recognition System

This article describes the design and application of a speech emotion recognition system. The system is trained in Czech emotionally coloured speech. The output of the system is the evaluation one of the four emotional states. Classification of emotion from extracted features is performed by a multi-classifier whose structure consists of three sub-classifiers fusion by Bayes Believe rule. The proposed system was deployed in the Secure Mobile Communication Infrastructure developed for the Czech Republic Security Components.
High-Efficiency Deep-Ultraviolet Light-Emitting Diodes With Efficient Carrier Confinement and High Light Extraction

In deep-ultraviolet (DUV) light-emitting diodes (LEDs), it is difficult to obtain both efficient carrier confinement and high light extraction, which are quite sensitive to optical polarization and other physical parameters. In this paper, characteristics of DUV LEDs with various n-AlGaN layers and quantum barriers (QBs), and various widths of quantum wells (QWs) are investigated. Specifically, the capability of carrier confinement and properties of optical polarization are analyzed in detail. The simulation results show that LED structure with Al0.64Ga0. 36N QBs, n-Al0.7Ga0. 3N layer, and 4-nm-thick QWs, which has a peak emission wavelength of 284.5 nm at 60 mA, exhibits high internal quantum efficiency of 25% and high degree of optical polarization of 0.874.
Machine Learning-Based Prediction of Prolonged Length of Stay in Newborns

The ability to predict prolonged length of hospital stay for newborn children has clinical value as an indicator of newborn health status but also can assist in such health system resource considerations as improved utilization of hospital wards and beds. In this paper, we describe the application of machine learning-based prediction to a Healthcare Cost and Utilization Project dataset and report on the performance of various developed predictive models. Via only utilizing administrative data and minimal clinical data available near to the time of admission/birth, we are able to demonstrate high performing models. The use of HCUP data for building newborn prolonged length of stay models potentially applicable across health care providers is an important contribution, and additionally the models represent high-performing models in the field of published predictive models of newborn length of stay in general.
Achieving a decision in antagonistic multi agent networks: frustration determines commitment strength*

In this work we consider a nonlinear interconnected system describing a decision-making process in a community of agents characterized by the coexistence of collaborative and antagonistic interactions. The resulting signed graph is in general not structurally balanced. It is shown in the paper that the decision-making process is affected by the frustration of the signed graph, in the sense that a nontrivial decision can be reached only if the social commitment of the agents is high enough to win the disorder introduced by the frustration in the network. The higher the frustration of the graph, the higher the commitment strength required from the agents.
Dual Band Edge Wall Slotted Array Antenna with Reduced Cross Polarization at X Band

A novel dual-band edge-wall slotted array antenna with reduced cross-polarization is proposed for X-band applications. A new vertical wrap up has been used with narrow wall slots for designing the array. Two frequencies of resonance are observed at 8.64GHz and 10.10GHz respectively with suitable matching performances. Metallic flares are introduced on both the sides of the waveguide for the purpose of reducing the cross-pol radiation and the results are compared. A reduction of around 10dB in the cross-pol levels are observed due to the inclusion of the additional flares. The simulated gains at both the frequencies of operation were found to be 16.79dB at 10.10GHz frequency and 15.39dB at 8.64 GHz frequency- which demonstrates that the proposed antenna can be used for dual band applications.
Multi-Class Coded Layered Asymmetrically Clipped Optical OFDM

Multi-class channel coded layered asymmetrically clipped optical orthogonal frequency-division multiplexing (LACO-OFDM) is proposed, where the achievable rate of the system is derived based on our mutual information analysis. We conceive a multi-class channel encoding scheme integrated with the layered transmitter. At the receiver, both the coded and uncoded likelihood ratios are extracted for inter-layer interference cancellation and symbol detection, respectively. Simulations are conducted, and the results show that our design approaches the achievable rate within 1.1 dB for 16-QAM four-layer LACO-OFDM with the aid of a half-rate eight-iteration turbo code at $\text {BER}=10^{-3}$ , outperforming its conventional counterpart by about 3.6 dB.
Passivity and Evolutionary Game Dynamics

This paper investigates an energy conservation and dissipation – passivity – aspect of dynamic models in evolutionary game theory. We define a notion of passivity using the state-space representation of the models. Our main contributions include devising systematic methods to examine passivity and identifying properties of passive dynamic models. We explain how our main results can be used to establish a connection between passivity and stability of equilibrium in population games and provide numerical simulations to illustrate stability in population games.
Robust Neuro-Adaptive Containment of Multileader Multiagent Systems With Uncertain Dynamics

One typical reflection of our understanding on multiagent systems (MASs) is our ability to design the emergence mechanism responsible for their various cooperative behaviors. This paper is concerned with the cooperative robust containment control problem of multileader MASs subject to unknown nonlinear dynamics and external disturbances. Specifically, quasi-containment and asymptotic containment problems are, respectively, considered by using tools from neural network (NN) approximation theory and Lyapunov stability theory of nonsmooth systems. A new kind of containment controllers consisting of a linear local information-based feedback term, a neuro-adaptive approximation term and a nonsmooth feedback term are designed to complete the goal of quasi-containment. Under the assumption that the subgraph depicting the coupling configuration among followers is detail-balanced and each follower can be influenced by at least one leader, it is proven that the containment error vector of the closed-loop MASs will be uniformly ultimately bounded if the control parameters of the proposed containment controllers are suitably designed. By introducing a pseudo ideal weighting matrix for NN approximator embedded at each follower, a novel class of containment controllers are further designed to precisely achieve asymptotic containment in the considered MASs where the Euclidean norm of containment error vector asymptotically converges to zero. At last, numerical simulations are given to verify the validity of these derived theoretical results.
Visual-Inertial SLAM Initialization: A General Linear Formulation and a Gravity-Observing Non-Linear Optimization

The initialization is one of the less reliable pieces of Visual-Inertial SLAM (VI-SLAM) and Odometry (VI-O). The estimation of the initial state (camera poses, IMU states and landmark positions) from the first data readings lacks the accuracy and robustness of other parts of the pipeline, and most algorithms have high failure rates and/or initialization delays up to tens of seconds. Such initialization is critical for AR systems, as the failures and delays of the current approaches can ruin the user experience or mandate impractical guided calibration. In this paper we address the state initialization problem using a monocular-inertial sensor setup, the most common in AR platforms. Our contributions are 1) a general linear formulation to obtain an initialization seed, and 2) a non-linear optimization scheme, including gravity, to refine the seed. Our experimental results, in a public dataset, show that our approach improves the accuracy and robustness of current VI state initialization schemes.
Novel Solution for High-Temperature Dielectric Application to Encapsulate High-Voltage Power Semiconductor Devices

Traditional semiconductor packaging techniques and materials have been working well for conventional Si devices, which usually operate at temperatures up to 175 °C. As the operating temperature increases, these techniques exhibit failures such as bulk flows, volume shrinkage, brittleness and subsequent cracking, and deterioration of dielectric strength. For the new wide-bandgap power devices, which work at voltages as high as 1200 V and junction temperatures as high as 250 °C, there is currently no known dielectric material to encapsulate and protect the active devices. This paper summarizes novel solutions for high-temperature dielectric materials for encapsulating high-power semiconductor devices without any dielectric breakdown and also without introducing excessive thermal, electrical, and mechanical stresses to the encapsulated devices. Polymer dielectric candidates investigated in this paper include cyanate ester-based resin and silicone-based resin. In addition, conformal coating approaches that include alumina deposited by atomic layer deposition (ALD) technique and tetrahedral amorphous carbon (ta-c) deposited by filtered cathodic vacuum arc technique were evaluated in this paper. Dielectric strength performance of the material combinations with respect to temperatures was evaluated. Among the polymer encapsulants, a silicone resin with silica fillers was determined to be the prospective candidate. Breakdown voltage and leakage current of the silicone-based encapsulant with and without the conformal coating was measured by breakdown tests. It was determined that the ta-c conformal coating deteriorates the dielectric performance of the encapsulant, while the alumina thin film deposited by ALD approach reduces the leakage current of the encapsulation material and also increases the breakdown voltage of the silicone encapsulant. Thus, the combination of alumina thin film deposited by ALD approach along with the silicone encapsulant is recommended for this application, involving high temperature and high voltage.
Training Region Selector for Gram Stained Slides with Limited Data: A Data Distillation Approach

In this paper, we tackle the region selection task for the purpose of whole slide image (WSI) analysis with a focus on Gram stained slides. Typically, the number of tiles required to capture using high magnification objective is extremely large. Thus, in practice, we need to perform region of interest selection in low magnification objectives to choose only the best candidate regions for the later high magnification analysis. With the fast development of computer vision and deep learning, it is possible to train an accurate convolutional neural network (CNN) based classifier to do this region selection task which would normally be done by experienced scientists and pathologists. However, data collection and labelling during the training stage are very time-consuming, labour intensive and expensive tasks. Researchers starting to ask a much more challenging question: how can we use unlabeled data to boost the system performance? In this paper, to answer this question and to reduce labelling effort, we propose a data distillation training framework to train a CNN classifier on limited labeled data with the help of unlabeled data. The unlabeled data will be carefully selected by a teacher model via a data distillation process and put into the training set for a student model. Extensive experiments show that the proposed framework achieves a notable gain in accuracy.
Toward a Perpetual IoT System: Wireless Power Management Policy With Threshold Structure

With the advancement of wireless energy harvesting and transfer techniques, an Internet of Things (IoT) node equipped with a wireless charging facility can request and receive energy from wireless chargers deployed at different locations. This provides more opportunity for the mobile IoT node to replenish its battery and be able to operate without interruption due to shortage of energy supply. In this paper, we develop an optimal energy charging scheme for the mobile IoT node, considering the states of location, traffic generation, and energy storage. We formulate the problem of energy charging as a Markov decision process (MDP) to obtain the mobile IoT node’s optimal policy. The objective is to maximize the expected utility. Furthermore, we prove that the optimal policy of the proposed MDP has a threshold structure. The numerical results show the performances of the mobile IoT node under various scenarios and parameter setting. Furthermore, the proposed MDP-based wireless energy charging scheme outperforms conventional baseline schemes.
Lattice-Traversing Design Space Exploration for High Level Synthesis

This paper describes a design space exploration methodology for High Level Synthesis (HLS) frameworks. Inputs of HLS tools are a description (usually in C/C++) of the functionality of an intended hardware, and a set of optimisation directives that specify its implementation, hence allowing the generation of many design variants with widely varying performance and required resources. The relationship between directives and performance/cost is nonetheless not straightforward, and highly influenced by application-specific characteristics. A major challenge facing designers is then to define effective values for the directives while avoiding time-consuming – and often infeasible – exhaustive explorations. We herein address it by proposing a novel HLS exploration approach which employs a lattice representation of the design space, and a methodology for its navigation. We base our strategy on the observation that Pareto-implementations share a low variance among their configurations. We therefore guide the selection of HLS directives minimising the variance of new candidate solutions, with respect to the best performing ones that have already been visited. By only requiring local searches in the lattice space, our methodology gracefully scales to complex designs. It results in close approximations of the real Pareto frontier, while requiring a lower workload and fewer synthesis runs with respect to existing approaches.