Parallel Work Research Articles

Salient object detection for RGBD video via spatial interaction and depth-based boundary refinement

Recently proposed state-of-the-art saliency detection models rely heavily on labeled datasets and rarely focus on perfect RGBD feature fusion, which lowers their generalization ability. In this paper, we propose a depth-based interaction and refinement network (DIR-Net) to fully leverage the depth information provided with RGB images to generate and refine the corresponding saliency segmentation maps. In total, three modules are included in our framework. A depth-based refinement module (DRM) and an RGB module work in parallel while coordinating via interactive spatial guidance modules (ISGMs), which utilize spatial and channel attention computed from both depth features and RGB features. In each layer, the features in both modules are refined and guided by the spatial information obtained from the other module through ISGMs. In the RGB module, before sending the depth-guided feature map to the decoder, a convolutional gated recurrent unit (ConvGRU)-based block is introduced to handle temporal information. Thinking about the clear movement information in RGB features, the block also guides temporal information in DRM. By merging the results from both the DRM and RGB modules, a segmentation map with distinct boundaries is generated. Considering the lack of depth images in popular public datasets, we utilize a depth estimation network that incorporates manual postprocessing-based correction to generate depth images on the DAVIS and UVSD datasets. The state-of-the-art performance achieved on both the original and new datasets illustrates the advantage of our RGBD feature fusion strategy, with a real-time speed of 19 fps on a single GPU.

Semantic Knowledge-Based Hierarchical Planning Approach for Multi-Robot Systems

Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the movement of the robots and the influence of each robot. For this purpose, researchers have proposed various methods for modeling and planning multi-robot missions. In particular, some approaches have been presented for high-level task planning by introducing semantic knowledge, such as relationships and domain rules, for environmental factors. This paper proposes a semantic knowledge-based hierarchical planning approach for multi-robot systems. We extend the semantic knowledge by considering the influence and interaction between environmental elements in multi-robot systems. Relationship knowledge represents the space occupancy of each environmental element and the possession of objects. Additionally, the knowledge property is defined to express the hierarchical information of each space. Based on the suggested semantic knowledge, the task planner utilizes spatial hierarchy knowledge to group the robots and generate optimal task plans for each group. With this approach, our method efficiently plans complex missions while handling overlap and deadlock problems among the robots. The experiments verified the feasibility of the suggested semantic knowledge and demonstrated that the task planner could reduce the planning time in simulation environments.

Parallel or Cross? Effects of Two Collaborative Modes on Augmented Reality Co-located Operations

Augmented reality (AR) can bring a new interactive experience to the collaboration between users. When users are in the same place, there are two modes of joint operation for the same object: parallel-work (PW) and cross-work (CW). PW means two users perform their tasks, while CW means assisting each other. To investigate the difference that collaboration using PW and CW in an AR environment brings to users, we developed a two-person local collaboration system, LoCol. We designed and conducted user experiments by selecting the tasks of adjusting the virtual model of the assembly and adding missing boundaries in the model. The results showed that CW led to a higher sense of social coexistence while reducing workload. In terms of task completion time and accuracy, CW and PW each had advantages. We found that users generally want to reduce unnecessary repetitive operations and frequent movement by working with others. This is likely an important criterion for determining who is better suited for a particular job in either approach.

Do Infrared Thermometers Hold Promise for an Effective Early Warning System for Emerging Respiratory Infectious Diseases?

Major respiratory infectious diseases, such as influenza, SARS-CoV, and SARS-CoV-2, have caused historic global pandemics with severe disease and economic burdens. Early warning and timely intervention are key to suppress such outbreaks. We propose a theoretical framework for a community-based early warning (EWS) system that will proactively detect temperature abnormalities in the community based on a collective network of infrared thermometer-enabled smartphone devices. We developed a framework for a community-based EWS and demonstrated its operation with a schematic flowchart. We emphasize the potential feasibility of the EWS and potential obstacles. Overall, the framework uses advanced artificial intelligence (AI) technology on cloud computing platforms to identify the probability of an outbreak in a timely manner. It hinges on the detection of geospatial temperature abnormalities in the community based on mass data collection, cloud-based computing and analysis, decision-making, and feedback. The EWS may be feasible for implementation considering its public acceptance, technical practicality, and value for money. However, it is important that the proposed framework work in parallel or in combination with other early warning mechanisms due to a relatively long initial model training process. The framework, if implemented, may provide an important tool for important decisions for early prevention and control of respiratory diseases for health stakeholders.

A Review of the Research on Film and Television Literature on the Theme of Contemporary School Bullying in China

Whether it is the original novel or the adapted film and television literary script, the creation of film and television literary works on the theme of school bullying has been favored by writers and loved by readers. In the field of literary criticism, it is not uncommon to study the theme of contemporary Chinese school bullying film and television literature. As an important branch of contemporary literary research, contemporary film and television literature research carries the mission, responsibility and value path of contemporary literary speech society, and the research on authenticity from society has always been the focus of research in the field of humanities and social sciences. Starting from the existing research results of the school bullying series of film and television literary works, this paper reviews the two major aspects of case study and comparative research, and the two research perspectives within and outside the text, and compares the domestic film and television bullying literature and Chinese and foreign works in parallel through the research method of comparative research, and conducts a comprehensive, all-round and systematic review of the "research" of the nature of review. It aims to call on the world to pay more attention to this real moral and ethical issue, explore effective solutions to problems, guide practice with theory, and promote the construction of a harmonious society with the spark of academia.

Measurement of the Satisfaction Level of the Country’s Pharmaceutical Industry with Suppliers of Raw Materials in This Industry Using System Dynamics

Background: The success of the supply chain can be summed up in the satisfaction level of business partners and customers, according to several diverse needs of each of the business partners in the studied supply chain, including the country’s pharmaceutical industry and suppliers of raw materials.
 Methods: This study has attempted to investigate satisfaction according to the views under possible scenarios to improve satisfaction. The model presented in this regard to realize the main objective of this study is based on the principles of system dynamics, and VENSIM DSS has been used for its design.
 Results: according to the study results, it can be acknowledged that the satisfaction of each of the business partners with the supply chain is different from each other, and the best scenarios to improve the satisfaction include an improvement of 3% in inventory management and a reduction by 5% in rework and parallel work during the studied period.
 Conclusions: Under this scenario, the satisfaction of business partners in this regard will improve during the studied period, or in other words, the satisfaction of the parties will move optimally.

Research gaps and opportunities in autoimmune hepatitis-Results of the international autoimmune hepatitis group research workshop 2022.

Autoimmune hepatitis (AIH) is a rare autoimmune liver disease that is characterised by a chronic inflammatory immune reaction directed against hepatocytes. The disease results in a substantial reduction in quality of life and potentially leads to liver-related complications or death. The International Autoimmune Hepatitis Group (IAIHG) initiated a series of research workshops to uncover the scientific gaps and opportunities in AIH. This review summarises the results of the latest workshop in Maastricht in 2022 and reviews the current challenges in adult AIH, particularly in relation to four important aspects of AIH: diagnostics; new immunomodulatory therapies; clinical trial design; and unmet clinical needs. This review also summarises the progress made since the AIH workshop in 2017. Patients and patient representatives were actively involved in the parallel working groups alongside clinicians and researchers. Despite 40 years of experience with diagnosing and treating AIH, false diagnoses occur and treatment is still based on nonselective immunosuppression. In addition to the need for more specific diagnostic tests, prognostic markers and tailor-based treatments, a major unmet clinical need was identified in areas of care delivery and health-related quality of life.

Mobile Robot Path Following Controller Based On the Sirms Dynamically Connected Fuzzy Inference Model

In this paper, the single input rule modules (SIRMs) dynamically connected fuzzy inference model is employed to design a path following controller for a unicycle wheeled mobile robot(WMR).The SIRMs model is mainly used to reduce the number of fuzzy inference rules and consequently reduce the processing time of the conventional Fuzzy Logic Control (FLC) schemes. The adopted path following strategy uses two control unites working in parallel to drive the WMR to follow a path composed of a succession of discrete waypoints. The first unit is a heading controller charged of generatingthe angular velocity command and thesecond one is a linear velocity controller charged of the generation the speed control signal.For the headingcontroller, all input variables are assigned with two fuzzy inference modules: a SIRM and a dynamic importancedegree (DID). The structure of the SIRMs based linear velocitycontrolunitwas modified to simplify the designand to fulfill the requirements of the path following strategy. It contains two SIRM modules andonecommonDID. To ensure the stability of the WMR'smotion, the SIRMs and the DIDs in the two control units were designedin such a manner that the control of the robot's orientationgets higher priority over the control of the WMRspeed. The use of the SIRM fuzzy inference model allows the online automatic adjustment of priority orders of thedifferent control actions. No optimizationof the controller parameters is required since thatall base values areset to 0 and all the breadthsare set to 1 for the DID modules. This means that all the input items play equal rolesin the control of the WMR.The structure of the proposedpath following controller is simple and the inferencerules are intuitively understandable because they are inspired from the knowledge of a human expert. Numericalsimulations conducted in the MATLABenvironment show that the proposed SIRMs based controller outperforms apath followingcontroller that uses the conventional FLC scheme.

A parallel particle swarm optimization and enhanced sparrow search algorithm for unmanned aerial vehicle path planning

Unmanned Aerial Vehicle (UAV) path planning is to plan an optimal path for its flight in a specific environment. But it cannot get satisfactory results using ordinary algorithms. To solve this problem, a hybrid algorithm is proposed named as PESSA, where particle swarm optimization (PSO) and an enhanced sparrow search algorithm (ESSA) work in parallel. In the ESSA, the random jump of the producer’s position is strengthened to guarantee the global search ability. Each scrounger keeps learning from the vintage experience of the producers. For the best-positioned sparrow, when it perceives the threat, the difference between the best individual and the worst individual will be imposed to speed up the search process. The elite reverse search strategy was added to yields the optimum diversity. In this paper, the performance of the PESSA algorithm is verified by 10 basic functions, and it can find the optimal value on the 7 test functions. Compared with the other 12 algorithms, PESSA’s average value always ranks first. Finally, the proposed PESSA is applied in 4 different scenarios including two groups of 2D environments and two groups of 3D environments. In 2D environments, the average optimization results can reach 0.0165 and 0.0521 in two cases respectively. In 3D environments, the average optimization results can reach 0.6635 and 0.5349 in two cases respectively. The results show that the PESSA algorithm can acquire more feasible and effective route than compared algorithms.

TopSort: A High-Performance Two-Phase Sorting Accelerator Optimized on HBM-Based FPGAs

The emergence of high-bandwidth memory (HBM) brings new opportunities to boost the performance of sorting acceleration on FPGAs, which was conventionally bounded by the available off-chip memory bandwidth. However, it is nontrivial for designers to fully utilize this immense bandwidth. First, the existing sorter designs cannot be directly scaled at the increasing rate of available off-chip bandwidth, as the required on-chip resource usage grows at a much faster rate and would bound the sorting performance in turn. Second, designers need an in-depth understanding of HBM's characteristics to effectively utilize the HBM bandwidth. To tackle these challenges, we present TopSort, a novel two-phase sorting solution optimized for HBM-based FPGAs. In the first phase, 16 merge trees work in parallel to fully utilize 32 HBM channels' bandwidth. In the second phase, TopSort reuses the logic from phase one to form a wider merge tree to merge the partially sorted results from phase one. TopSort also adopts HBM-specific optimizations to reduce resource overhead and improve bandwidth utilization. TopSort can sort up to 4 GB data using all 32 HBM channels, with an overall sorting performance of 15.6 GB/s. TopSort is 6.7× and 2.7× faster than state-of-the-art CPU and FPGA sorters.

A Dual Observer for Parameter-Free Encoderless Control of Doubly-Fed Reluctance Generators

A novel, cascaded model reference adaptive system (MRAS) observer design for speed and reactive power ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{Q}$</tex-math></inline-formula> ) control of the emerging brushless doubly-fed reluctance generator (BDFRG) without a shaft position sensor, and no machine parameter knowledge, is proposed. The main advantages of the underlying estimation technique over the existing ones reported in the open literature are the intrinsic versatility, robustness, and accuracy, offering entirely decoupled torque (power) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{Q}$</tex-math></inline-formula> responses. The reference model of the rotor angular velocity and position MRAS observer is purely based on the current measurements of the converter-fed (secondary) winding, and its adaptive counterpart on the measured grid voltages and currents at line frequency. The latter requires the inductance ratio for calculations, the online estimates of which being generated by the supplementary MRAS observer working in parallel. Doing so, a complete machine parameter independence and stability of the scheme have been achieved, and its viability fully experimentally validated under variable loading conditions of the small-scale BDFRG wind turbine emulated in a laboratory environment.

Modeling GPU Dynamic Parallelism for self similar density workloads

Dynamic Parallelism (DP) is a GPU programming abstraction that can make parallel computation more efficient for problems that exhibit heterogeneous workloads. With DP, GPU threads can launch kernels with more threads, recursively, producing a subdivision effect where resources are focused on the regions that exhibit more parallel work. Doing an optimal subdivision process is not trivial, as the combination of different parameters play a relevant role in the final performance of DP. Also, the current programming abstraction of DP relies on kernel recursion, which has performance overhead. This work presents a new subdivision cost model for problems that exhibit self similar density (SSD) workloads, useful for finding efficient subdivision schemes. Also, a new subdivision implementation free of recursion overhead is presented, named Adaptive Serial Kernels (ASK). Using the Mandelbrot set as a case study, the cost model shows that optimal performance is achieved when using {g∼32,r∼2,B∼32} for the initial subdivision, recurrent subdivision and stopping size, respectively. Experimental results agree with the theoretical parameters, confirming the usability of the cost model. In terms of performance, the ASK approach runs up to ∼60% faster than DP in the Mandelbrot set, and up to 12× faster than a basic exhaustive implementation, whereas DP is up to 7.5× faster. In terms of energy efficiency, ASK is up to ∼2× and ∼20× more energy efficient than DP and the exhaustive approach, respectively. These results put the subdivision cost model and the ASK approach as useful tools for analyzing the potential improvement of subdivision based approaches and for developing more efficient GPU-based libraries or fine-tune specific codes in research teams.

Identifying and prioritizing the factors affecting the knowledge flow in high-tech industries

PurposeThe purpose of this study paper is to identify and prioritize the factors affecting the knowledge flow in high-tech industries.Design/methodology/approachThis research is applied in terms of purpose and descriptive-survey in terms of data collection method. This research has been done in a qualitative–quantitative method. In the qualitative part, due to the nature of the data in this study, expert interviews have been used. The sample studied in this research includes 35 managers and expert professors with experience in the field of knowledge management working in universities and high-tech industries who have been selected by the method of snowball. In the quantitative part, the questionnaire tool and DANP multivariate decision-making method have been used.FindingsIn this study, a multicriteria decision-making technique using a combination of DEMATEL and ANP (DANP) was used to identify and prioritize the factors affecting the knowledge flow in high-tech industries. In this study, the factors affecting the knowledge flow, including 8 main factors and 31 subfactors, were selected. Human resources, organizational structure, organizational culture, knowledge communication, knowledge management tools, knowledge characteristics, laws, policies and regulations and financial resources were effective in improving knowledge flow, respectively.Originality/valueBy studying the research, it was found that the study area is limited, and the previous work has remained at the level of documentation and little practical use has been done. In previous research, the discussion of knowledge flow has not been very open, and doing incomplete work causes limited experiences and increases cost and time wastage, and parallel work may also occur. Therefore, to complete the knowledge management circle and fully achieve the research objectives, as well as to make available and transfer the experiences of people working in this field and also to save time and reduce costs, the contents and factors of previous models have been counted. It is designed for high-tech industries, a model for the flow of knowledge.

A new deterministic PSO algorithm for real-time systems implemented on low-power devices

The paper presents a novel, deterministic particle swarm optimization (PSO) algorithm, facilitating its implementation in low-power application specific integrated circuits (ASIC) realized in CMOS technology. The PSO algorithms are commonly used in a variety of optimization problems. They allow to search for a global extreme in a selected fitness function, which is used for numerical modeling of specific optimized problems. The conventional version of the algorithm is relatively difficult to implement directly in hardware, especially when each particle is represented by a separate circuit working in parallel with other particles. In such a situation, each particle has to be equipped with its own copy of each function. One of the problems is, for example, an efficient realization of the random function block that provides for each particle some factors used in computing a cognitive and a social velocity components. In this paper we shows a novel way to work around this problem. In the proposed approach the random function does not need to be used. It has been replaced by a simple computationally deterministic mechanism, which at the level of the overall swarm allows for effective emulation of the former random mechanism. We proposed such simplifications that reduce the formulas describing the algorithm to only basic arithmetic and logic operations. Comprehensive investigations have shown that performance of the proposed approach is comparable with its original version or in may situations even better. The proposed algorithm was tested with numerous test functions (50 runs for each tested function), to verify its flexibility and adaptation abilities to different problems.

Comparison Between Different Droop Based Control Techniques and a Virtual Control Oscillator

This work presents a literature review about control techniques for parallel connected power inverters under microgrid applications. Some control strategies, based on droop control for parallel inverters of distributed generation units in an ac distribution system will be presented in this work. Finally, an important method called Virtual Oscillating Control (VOC) is suggested for connecting voltage source inverters. Inverters are able to work in parallel with a constant-voltage constant frequency system, as well as with other inverters and also in standalone operation. The different power sources can share the load also under unbalanced conditions. Throughout this work several simulation results are presented in order to demonstrate the behaviour the behavior of the different control strategies tested.

Learning rates of multitask kernel methods

Learning theory aims at building a solid mathematical foundation for machine learning. A core objective of learning theory is to estimate the learning rates of various learning algorithms in order to analyze their generalization ability. By far, most such research efforts have been focusing on single‐task kernel methods. There is little parallel work on learning rates of multitask kernel methods. We shall present an analysis of the learning rates for multitask regularization networks and ‐norm coefficient regularization. Compared to the existing work on learning rate estimates of multitask regularization networks, our study is more applicable in that we do not require the regression function to lie in the vector‐valued reproducing kernel Hilbert space of the chosen matrix‐valued reproducing kernel. Our work on the learning rate of multitask ‐norm coefficient regularization is new. For both methods, our results reveal a quantitative dependency of the learning rates on the number of tasks, which is also new in the literature.

Analytical Model of Structural Damping in Friction Module of Shell Shock Absorber Connected to Spring

Vibration processes play a significant role in the modern industry. In most cases, vibration reduces strength, reliability, and durability of industrial machines, mechanisms, and structures, as well as adversely affects the health of support staff. This study presents a new dry friction shell shock absorber design. The shock absorber contains two spring sections and a friction module with an open shell and an elastic filler; at the same time, the spring sections and the friction module work in parallel. The proposed device demonstrates good damping characteristics, capable of operating under high operating loads, and at the same time has compact transverse dimensions. With a nonmonotonic loading of such a shock absorber, due to the contact interaction of the filler with an open shell, part of the energy that is supplied to the system will be dissipated. In other words, in response to the action of an external nonmonotonic loading, the phenomenon of structural hysteresis occurs in the friction module of the shock absorber. To describe the deformation of the shock absorber, a mechanical and mathematical model of a shell with a cut along the generatrix, which is the main bearing link, has been developed. By means of the technique of quasistatic analysis of structural damping in nonmobile nonconservative shell systems with a deformable filler, the hysteresis loops of the presented shock absorber are analytically described. Using them, according to the known loading history, it is possible to predict the behavior of the considered nonconservative system at any time after the start of the loading process. At each stage of the cyclic loading, the distribution of stresses and relationships between the external loading and the piston displacement was studied. Inequalities are obtained for permissible loads under which the operation of the shock absorber is safe. Such shell shock absorbers are projected to be used in the mining, oil and gas, and construction industries.

Stable discontinuous stationary solutions to reaction-diffusion-ODE systems

A general system of n ordinary differential equations coupled with one reaction-diffusion equation, considered in a bounded N-dimensional domain, with no-flux boundary condition is studied in a context of pattern formation. Such initial boundary value problems may have different types of stationary solutions. In our parallel work [Instability of all regular stationary solutions to reaction-diffusion-ODE systems (2021)], regular (i.e. sufficiently smooth) stationary solutions are shown to exist, however, all of them are unstable. The goal of this work is to construct discontinuous stationary solutions to general reaction-diffusion-ODE systems and to find sufficient conditions for their stability.

On the Application of Quick Artificial Bee Colony Algorithm (qABC) for Attenuation of Test Suite in Real-Time Software Applications

Software testing plays a vital role during the software development process, as it ensures quality software deployment. Success of software testing depends on the design of effective test cases. To achieve the optimization of generated test cases, the proposed approach combines both global and local searches by means of intelligent agents which exhibit the behaviour of employed bees, onlooker bees, and scout bees in the qABC algorithm. The proposed qABC algorithm has key improvements over the basic artificial bee colony algorithm (ABC) in test optimization by reducing redundancy, filtering of test cases in each iteration and parallel working of the bees. Further, the fitness evaluation of the test cases is done by employing two test adequacy metrics namely path coverage and mutation score. Further, the experimental evaluation of qABC, GA, and the basic ABC based test cases is done using several case study applications. The result shows that qABC outperforms the other algorithms in terms of effectiveness of test cases in revealing the faults with less time and a smaller number of test cases.

Exploring Memory Access Similarity to Improve Irregular Application Performance for Distributed Hybrid Memory Systems

With the increasing problem complexity, more irregular applications are deployed on high-performance clusters due to the parallel working paradigm, and yield irregular memory access behaviors across nodes. However, the irregularity of memory access behaviors is not comprehensively studied, which results in low utilization of the integrated hybrid memory system compositing of stacked DRAM and off-chip DRAM. To address this problem, we devise a novel method called <i>Similarity-Managed Hybrid Memory System</i> ( <i>SM-HMS</i> ) to improve the hybrid memory system performance by leveraging the memory access similarity among nodes in a cluster. Within <i>SM-HMS</i> , two techniques are proposed, <i>Memory Access Similarity Measuring</i> and <i>Similarity-based Memory Access Behavior Sharing</i> . To quantify the memory access similarity, memory access behaviors of each node are vectorized, and the distance between two vectors is used as the memory access similarity. The calculated memory access similarity is used to share memory access behaviors precisely across nodes. With the shared memory access behaviors, <i>SM-HMS</i> divides the stacked DRAM into two sections, the <i>sliding window section</i> and the <i>outlier section</i> . The shared memory access behaviors guide the replacement of the <i>sliding window section</i> while the <i>outlier section</i> is managed in the LRU manner. Our evaluation results with a set of irregular applications on various clusters consisting of up to 256 nodes have shown that <i>SM-HMS</i> outperforms the state-of-the-art approaches, <i>Cameo</i> , <i>Chameleon</i> , and <i>Hyrbid2</i> , on job finish time reduction by up to <inline-formula><tex-math notation="LaTeX">$58.6\%$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$56.7\%$</tex-math></inline-formula> , and <inline-formula><tex-math notation="LaTeX">$31.3\%$</tex-math></inline-formula> , with <inline-formula><tex-math notation="LaTeX">$46.1\%$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$41.6\%$</tex-math></inline-formula> , and <inline-formula><tex-math notation="LaTeX">$19.3\%$</tex-math></inline-formula> on average, respectively. <i>SM-HMS</i> can also achieve up to <inline-formula><tex-math notation="LaTeX">$98.6\%$</tex-math></inline-formula> ( <inline-formula><tex-math notation="LaTeX">$91.9\%$</tex-math></inline-formula> on average) of the ideal hybrid memory system performance.