Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-efficient frozen food transports: the Refrigerated Routing Problem

Given the growing importance of cold chains and the need to promote sustainable processes, energy efficiency in refrigerated transports is investigated at operational level. The Refrigerated Routing Problem is defined, involving multi-drop deliveries of palletised unit loads of frozen food from a central depot to clients. The objective is to select the route with minimum fuel consumption for both traction and refrigeration. The problem formulation considers speed variation due to traffic congestion phenomena, as well as decreasing load on board along the route as successive clients are visited. Transmission load for exposure of the vehicle to outdoor temperatures and infiltration load at door opening are modelled, taking into account outdoor conditions varying along the day and the year. The resulting multi-period problem is modelled and solved by means of Constraint Programming. Test scenarios come from a real local network for frozen bread dough distributed to supermarkets. Results show how fuel minimisation leads to the selection of different routes in comparison to the traditional total travel distance or time objectives. Energy savings are affected by demand distribution among the clients, departure time, number of visits per tour, seasonality and location of the delivery network.
Complementary projector for null-space stiffness control of redundant assembly robot arm

Purpose Stiffness control of redundant robot arm, aimed at using extra degrees of freedom (DoF) to shape the robot tool center point (TCP) elastomechanical behavior to be consistent with the essential requirements needed for a successful part mating process, i.e., to mimic part supporting mechanism with selective quasi-isotropic compliance (Remote Center of Compliance – RCC), with additional properties of inherent flexibility. Design/methodology/approach Theoretical analysis and synthesis of the complementary projector for null-space stiffness control of kinematically redundant robot arm. Practical feasibility of the proposed approach was proven by extensive computer simulations and physical experiments, based on commercially available 7 DoF SIA 10 F Yaskawa articulated robot arm, equipped with the open-architecture control system, system for generating excitation force, dedicated sensory system for displacement measurement and a system for real-time acquisition of sensory data. Findings Simulation experiments demonstrated convergence and stability of the proposed complementary projector. Physical experiments demonstrated that the proposed complementary projector can be implemented on the commercially available anthropomorphic redundant arm upgraded with open-architecture control system and that this projector has the capacity to efficiently affect the task-space TCP stiffness of the robot arm, with a satisfactory degree of consistency with the behavior obtained in the simulation experiments. Originality/value A novel complementary projector was synthesized based on the adopted objective function. Practical verification was conducted using computer simulations and physical experiments. For the needs of physical experiments, an adequate open-architecture control system was developed and upgraded through the implementation of the proposed complementary projector and an adequate system for generating excitation and measuring displacement of the robot TCP. Experiments demonstrated that the proposed complementary projector for null-space stiffness control is capable of producing the task-space TCP stiffness, which can satisfy the essential requirements needed for a successful part-mating process, thus allowing the redundant robot arm to mimic the RCC supporting mechanism behavior in a programmable manner.
3D modelling of a frame assembly using deep learning and the Chu–Liu–Edmonds Algorithm

Purpose The proposed method is to generate the 3 D model of frame assemblies based on their topological model automatedly. It was a very demanding task and there was no appropriate automated method to facilitate this work. Design/methodology/approach The proposed method includes two stages. The first stage is decisive. In this stage, a deep learning network and the Chu–Liu–Edmonds algorithm are used to recognize contact relations among parts. Based on this recognition, the authors perform a geometrical computation in the second stage to finalize the 3 D model. Findings The authors verify the feasibility of the proposed method using a case study and find that the classification rate of the deep learning network for part contact relations is higher than 75 per cent. Furthermore, more accurate results could be achieved with modification by the Chu–Liu–Edmonds algorithm. The proposed method has lower computational complexity compared with traditional heuristic methods, and its results are more consistent with existing designs. Research limitations/implications The paper introduces machine learning method into assembly modelling issue. The proposed method divides the assembly modelling into two steps and solves the assemble relation creatively. Practical implications Frame assemblies are fundamental to many areas. The proposed method could automate frame assembly modelling in a viable way. It could benefit design and manufacture process significantly. Originality/value The proposed method expands the application of machine learning into a new field. It would be more useful than simple machine learning in industry. The proposed method is better than general heuristic algorithms. It outputs identical results when the inputs are the same. Meanwhile, the algorithmic complexity in worst situation is better than general heuristic algorithms.
A Novel Multiuser Beamforming System With Reduced Complexity and Beam Optimizations

Analog beamforming is widely used in the incoming 5G/B5G communications systems to leverage against power consumption by significantly reducing the number of RF chains. In this paper, we propose a new multiuser beamforming scheme that allows the number of RF chains to be smaller than the number of users. In addition, we propose the beamforming algorithms to minimize the transmit power and weighted symbol error rates for the proposed scheme. For both high- and low-SNR regimes, the proposed beamforming designs provide simple closed-form solutions. The simulation results show that with half the number of RF chains and, thus, lower hardware cost, the proposed scheme outperforms the conventional zero-forcing beamforming systems in several interested parameter settings. Meanwhile, the performance degradation due to the quantization effect of the proposed system is less pronounced than that of the conventional beamforming systems.
High boost‐ratio bidirectional converter for interfacing low‐voltage battery energy storage system to a DC bus

Supply and demand mismatches in renewable energy systems are addressed by integrating battery banks. Selecting battery bank terminal voltage to match DC-bus voltage (350–450 V for single-phase AC loads), necessitates employing battery banks with long-string connections along with their attendant shortcomings. To employ short-string battery banks, high-boost-ratio bidirectional interfaces are required between the DC-bus and battery bank. Current literature lacks a single source where high-boost-ratio converters’ are categorised and their strengths and weaknesses identified. Comprehensive literature review is hence carried out to determine attributes of various high-boost-ratio DC–DC converters and also categorise them. The key attributes of a topology to interface battery storage to a DC-bus are determined. Based on these a bidirectional tapped-inductor boost converter emerges as the best candidate. Moreover, in order to regulate output voltage, voltage-gain versus duty-ratio characteristics should not be very steep. Since battery terminal voltage varies with state-of-charge, closed-loop control is necessary. Converter's small-signal transfer-functions are derived and a two-loop controller to regulate output voltage and inductor current while allowing bidirectional power flow designed. A novel bidirectional passive lossless snubber circuit is employed to clamp the voltage spikes across the active switches, without altering the normal operation of the converter.
Automating versus augmenting intelligence

ABSTRACT This article addresses the prospects for automating intelligence versus augmenting human intelligence. The evolution of artificial intelligence (AI) is summarized, including contemporary AI and the new capabilities now possible. Functional requirements to augment human intelligence are outlined. An overall architecture is presented for providing this functionality, including how it will make deep learning explainable to decision makers. Three case studies are addressed, including driverless cars, medical diagnosis, and insurance underwriting. Paths to transformation in these domains are discussed. Prospects for innovation are considered in terms of what we can now do, what we surely will be able to do soon, and what we are unlikely to ever be able to do.
DNN-Assisted Cooperative Localization in Vehicular Networks

This work develops a deep-learning-based cooperative localization technique for high localization accuracy and real-time operation in vehicular networks. In cooperative localization, the noisy observation of the pairwise distance and the angle between vehicles causes nonlinear optimization problems. To handle such a nonlinear optimization task at each vehicle, a deep neural network (DNN) technique is to replace a cumbersome solution of nonlinear optimization along with the saving of the computational loads. Simulation results demonstrate that the proposed technique attains some performance gain in localization accuracy and computational complexity as compared to existing cooperative localization techniques.
Compact UWB MIMO Antenna With High Isolation Using Fence-Type Decoupling Structure

In this letter, an ultrawideband (UWB) multiple-input–multiple-output (MIMO) antenna using a novel fence-type decoupling structure, which has high isolation in the UWB, is presented. The MIMO design consists of two half-cutting UWB antenna units that have good low-frequency impedance-matching performance because of the use of a rectangular slot on the radiation patch. Meanwhile, the isolation in the operating band is enhanced by introducing the fence-type decoupling structure at the ground of the antenna. Furthermore, the L-shaped parasitic branches are designed to heighten the impedance bandwidth and isolation in the low-frequency band, i.e., 3–3.4 GHz. The simulation and experiments show that the antenna has low mutual coupling (S21 < –25 dB) and a low envelope correlation coefficient (ECC < 0.004) in the UWB.
Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm

Subsynchronous oscillation (SSO) is a critical issue for the direct-drive permanent magnet synchronous generator (D-PMSG) based wind farm integrated to a weak onshore AC grid. To analyze the mechanism of the SSO phenomenon of D-PMSG based wind farm, widely used impedance-based stability analysis method is utilized in this paper. First, the impedance model based on the harmonic linearization theory of grid-connected D-PMSG is proposed, and the mechanism of sub/supsynchronous currents coupling is analyzed quantitatively for the first time. Then, based on the impedance model and relative stability criterion, the influence of wind farm operating parameters and grid impedance on stability is discussed. Simulations are carried out to verify the correctness of theoretical analysis.
Edge angle perception precision of active and passive touches for haptic VR using dot-matrix display

In haptic virtual reality (VR), there are two important challenges: one is the difficulty of reproducing a surface feeling with haptic devices, and the other is the presentation method used for the display. We developed a tactile mouse using palm presentation for the former challenge to tackle the issue, “which tactile perception is superior for texture recognition, active or passive touch? ” In a psychophysical experiment, two oblique edges of dot-patterns are presented consecutively as simple textures, and human subjects compare them to determine which is larger. We evaluated the precision of perception using the difference threshold of the edge angles calculated by the constant stimuli method. Experimental results show that the perception precision at low edge movement speeds (45 and 90 mm/s) was higher than that at high speeds (130 and 170 mm/s), and moreover, that there was no significant difference in precision of the edge angle perception between active and passive touches. The former finding seems to be caused by how well the mechanoreceptive unit handles uneven surfaces. The latter finding is due to the mechanism of the efference copy of a motor signal provided from the motor cortex not having a significant influence on texture recognition. The tactile image deterioration induced by the object movement might be compensated for with information processing in the central nervous system, which keeps stable tactile image without help from the efference copy. This work contributes to developing the haptic device which provides visually impaired persons with the tactile map.
Characterization of a Hybrid PM Generator Using a 32-Phase Brushless Excitation Scheme

This paper presents characterization and operational envelope of a hybrid permanent magnet (HPM) generator in a series hybrid electric vehicle (SHEV) powertrain. A proposed control strategy is applied to characterize the system during vehicle operation within new european driving cycle (NEDC). The HPM generator utilizes a permanent magnet topology assisted with a wound field (WF) rotor while the machine 9-phase output is rectified and connected to the SHEV main dc-link. A brushless exciter with dc stator and multiphase rotor is designed to provide an adjustable dc current and voltage to the HPM generator WF. The brushless exciter dc stator is fed from the vehicle dc-link using a dc/dc converter while its rotor output connects to an integrated 32-leg rotating rectifier the output of which provides a high quality dc to the HPM generator WF. A control strategy incorporating a dynamic model of the HPM generator, brushless exciter, and dc/dc converter is proposed under which the system operational envelope is obtained. The HPM generator efficiency map, torque speed, and WF current behavior is analyzed to realize an optimum operating region while the SHEV operates over the NEDC driving cycle. The analytically predicted results are validated against measurements from a laboratory prototype HPM generator and exciter system. The comparison shows the effectiveness of the control method and accuracy of the developed models.
Cultivating Sociomaterial Transformations in Agriculture 4.0: The Case of Precision Viticulture

Agriculture 4.0 is a growing field of research that aims to solve one of the most critical challenges for humanity: efficient production in a changing environment. This paper presents a two-year long research project in a wine producer with over 800 associates. The results provide an in-depth presentation of transformations in two vineyards using a ubiquitous system supported by smart sensors, machine-learning, and augmented reality. It also highlights the importance of preparing a sustainable strategy that includes (1) a farmer-centered design of agriculture 4.0, (2) an ecosystem of third-party entities that ensure coevolution with the producer, and (3) the added value of an increasing amount of data. Failing to achieve these recommendations may lead to a decrease in long-term adherence to digital transformation. The findings are relevant for companies that struggle worldwide with extreme threats of climate change and to maximize the return of their investments in agriculture 4.0.
A framework for robotic clothing assistance by imitation learning

ABSTRACT The recent demographic trend across developed nations shows a dramatic increase in the aging population, fallen fertility rates and a shortage of caregivers. Hence, the demand for service robots to assist with dressing which is an essential Activity of Daily Living (ADL) is increasing rapidly. Robotic Clothing Assistance is a challenging task since the robot has to deal with two demanding tasks simultaneously, (a) non-rigid and highly flexible cloth manipulation and (b) safe human–robot interaction while assisting humans whose posture may vary during the task. On the other hand, humans can deal with these tasks rather easily. In this paper, we propose a framework for robotic clothing assistance by imitation learning from a human demonstration to a compliant dual-arm robot. In this framework, we divide the dressing task into three phases, i.e. reaching phase, arm dressing phase, and body dressing phase. We model the arm dressing phase as a global trajectory modification using Dynamic Movement Primitives (DMP), while we model the body dressing phase toward a local trajectory modification applying Bayesian Gaussian Process Latent Variable Model (BGPLVM). We show that the proposed framework developed towards assisting the elderly is generalizable to various people and successfully performs a sleeveless shirt dressing task. We also present participants feedback on public demonstration at the International Robot Exhibition (iREX) 2017. To our knowledge, this is the first work performing a full dressing of a sleeveless shirt on a human subject with a humanoid robot. GRAPHICAL ABSTRACT
A Digital Diary: Remembering the Past Using the Present Context

Lifelog devices have gained much attention in recent past. These devices are capable of recording daily activities of a user such as visited places, calories burnt, heart rate, etc. However, reminiscing the past life from this huge collection has been less of a concern. We aim to assist in remembering similar past events based on the present context or situation. We designed a prototype lifelog device that captures lifelogs in the form of pictures and audio, and associates them with the device wearer’s context such as people and objects in the vicinity. The information from the past life may be used to helpusers in their current situation. In this article, we attempt to determine the type of context that is preferred by the device users to remember their past events. We found that the users are more interested to find lifelogs based on the people they meet at specific locations.
A robust grey wolf-based deep learning for brain tumour detection in MR images

Abstract In recent times, the detection of brain tumours has become more common. Generally, a brain tumour is an abnormal mass of tissue where the cells grow uncontrollably and are apparently unregulated by the mechanisms that control cells. A number of techniques have been developed thus far; however, the time needed in a detecting brain tumour is still a challenge in the field of image processing. This article proposes a new accurate detection model. The model includes certain processes such as preprocessing, segmentation, feature extraction and classification. Particularly, two extreme processes such as contrast enhancement and skull stripping are processed under the initial phase. In the segmentation process, we used the fuzzy means clustering (FCM) algorithm. Both the grey co-occurrence matrix (GLCM) as well as the grey-level run-length matrix (GRLM) features were extracted in the feature extraction phase. Moreover, this paper uses a deep belief network (DBN) for classification. The optimized DBN concept is used here, for which grey wolf optimisation (GWO) is used. The proposed model is termed the GW-DBN model. The proposed model compares its performance over other conventional methods in terms of accuracy, specificity, sensitivity, precision, negative predictive value (NPV), the F1Score and Matthews correlation coefficient (MCC), false negative rate (FNR), false positive rate (FPR) and false discovery rate (FDR), and proves the superiority of the proposed work.
Follow-up of restoration of works of art of the patrimony by infrared thermography

Infrared thermography is a non-destructive testing technique that affects many areas. This technique of analysis is, for example, very interesting in the field of restoration and conservation of heritage works. The possibilities of active thermography can help in the early detection of defects in works of art and their characterization. In this work we will demonstrate that it is possible to detect old restorations in frescoes and murals by flash method. This new possibility offered by thermography will make it possible, for example, to verify the compatibility of a restoration with the original work, which can prevent the appearance of a defect and may allow the conservator to follow up on restoration. First we will show the feasibility of this approach on a laboratory specimen containing different types of clogging materials and then present results of analysis carried out in situ at one restoration sites that reveal the possibility of locating these restorations but also to characterize the pictorial technique used in this one.
Electrically Compact SRR-Loaded Metamaterial Inspired Quad Band Antenna for Bluetooth/WiFi/WLAN/WiMAX System

In this paper, we reveal a concept of low-profile Split Ring Resonator loaded metamaterial inspired antenna for Bluetooth/WiFi/WLAN/WiMAX communication systems. The antenna’s overall dimensions are 30 × 31 mm2 where two metamaterial unit cells are placed parallel to each other and a zig-zag feed line is connected with the SubMiniature version A connector. The defected ground technique was used to improve the antenna’s operational bandwidth. The computer simulation technology Microwave Studio was used to design and perform the numerical investigation, and the antenna was fabricated on FR-4 dielectric material. The Agilent N5227A VNA and anechoic chamber-based Satimo Star Lab were used to measure the antenna’s scattering parameters, voltage standing wave ratio, gain, efficiency and radiation patterns. The proposed metamaterial antenna had 200 MHz (2.40–2.60 GHz) and 390 MHz (3.40–3.79 GHz) overall bandwidth, which are similar to the simulated data. The measured results were applicable for Bluetooth (2.40–2.485 GHz), WiFi (2.4 GHz), WLAN (2.40–2.49 GHz and 3.65–3.69 GHz), and WiMAX (3.40–3.79 GHz) applications. The antenna’s average gain was 1.50 dBi, with the maximum and minimum gains of 2.25 dBi and 0.88 dBi, respectively, in addition to omnidirectional radiation patterns at operating bands.