• 로봇학회논문지
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• 제16권1호
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• pp.56-63
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• 2021

As the use of robots in service area increases, research has been conducted to replace human tasks in daily life with robots. Among them, this study focuses on the tidy-up task on a desk using a robot arm. The order in which tidy-up motions are carried out has a great impact on the success rate of the task. Therefore, in this study, a neural network-based method for determining the priority of the tidy-up motions from the input image is proposed. Reinforcement learning, which shows good performance in the sequential decision-making process, is used to train such a task planner. The training process is conducted in a virtual tidy-up environment that is configured the same as the actual tidy-up environment. To transfer the learning results in the virtual environment to the actual environment, the input image is preprocessed into a segmented image. In addition, the use of a neural network that excludes unnecessary tidy-up motions from the priority during the tidy-up operation increases the success rate of the task planner. Experiments were conducted in the real world to verify the proposed task planning method.

• ETRI Journal
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• 제44권5호
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• pp.746-758
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• 2022

This paper focuses on a mobile edge-computing-enabled heterogeneous network. A battery level-aware task-scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery-powered mobile devices. The formulated optimization problem is a typical mixed-integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)-hard problem, a decomposition-based task-scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed-form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power-allocation subproblem is a unimodal problem; this subproblem is solved using a gradient-based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level-aware scheme on the operating hours of battery-powered mobile devices is also investigated.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.47-52
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• 2022

The current study investigates how student-teacher interaction can be developed through task-based teaching in undergraduate students' Saudi teaching and learning context. An experiment was conducted for five weeks on 85 male undergraduate students at a Saudi public university based in Jeddah, Saudi Arabia. The study investigated different types of student-teacher interaction through task-based teaching (speaking activities). The results revealed that the experimental group (43 students) evinced much more enthusiasm, willingness, engagement and readiness in their inclass participation than their peers in the control group (42 students). The student-teacher interaction also helped students to be more responsive to general and specific topics in speaking activities. The study recommends that decision-makers in education make student-teacher interaction part of the student's monthly assessment. It also recommends that more efforts be made to foster the awareness of students, teachers, and parents awareness of the academic and non-academic importance of interaction. One final recommendation of the research is that student-teacher interaction should be more emphasized and integrated into the school curriculum and adopted as a critical teaching strategy.

• ETRI Journal
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• 제45권1호
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• pp.93-104
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• 2023

Coreference resolution is a task in discourse analysis that links several headwords used in any document object. We suggest pointer networks-based coreference resolution for Korean using multi-task learning (MTL) with an attention mechanism for a hierarchical structure. As Korean is a head-final language, the head can easily be found. Our model learns the distribution by referring to the same entity position and utilizes a pointer network to conduct coreference resolution depending on the input headword. As the input is a document, the input sequence is very long. Thus, the core idea is to learn the word- and sentence-level distributions in parallel with MTL, while using a shared representation to address the long sequence problem. The suggested technique is used to generate word representations for Korean based on contextual information using pre-trained language models for Korean. In the same experimental conditions, our model performed roughly 1.8% better on CoNLL F1 than previous research without hierarchical structure.

• 로봇학회논문지
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• 제19권2호
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• pp.196-202
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• 2024

The ability to understand given environments and plan a sequence of actions leading to goal state is crucial for personal service robots. With recent advancements in deep learning, numerous studies have proposed methods for state representation in planning. However, previous works lack explicit information about relationships between objects when the state observation is converted to a single visual embedding containing all state information. In this paper, we introduce graph-based state representation that incorporates both object and relationship features. To leverage these advantages in addressing the task planning problem, we propose a Graph Neural Network (GNN)-based subgoal prediction model. This model can extract rich information about object and their interconnected relationships from given state graph. Moreover, a search-based algorithm is integrated with pre-trained subgoal prediction model and state transition module to explore diverse states and find proper sequence of subgoals. The proposed method is trained with synthetic task dataset collected in simulation environment, demonstrating a higher success rate with fewer additional searches compared to baseline methods.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제1권1호
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• pp.35-53
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• 1997

This paper analyzes the time complexity of Genetic Algorithm based Task Scheduling (GATS) which is designed for the scheduling of parallel programs with diverse embedded parallelism types in a heterogeneous network systems. The analysis of time complexity is performed based on two representation methods (REIA, REIS) which are proposed in this paper to encode the scheduling information. And the heterogeneous network systems consist of a set of loosely coupled parallel and vector machines connected via a high-speed network. The objective of heterogeneous network computing is to solve computationally intensive problems that have several types of parallelism, on a suite of high performance and parallel machines in a manner that best utilizes the capabilities of each machine. Therefore, when scheduling in heterogeneous network systems, the matching of the parallelism characteristics between tasks and parallel machines should be carefully handled in order to obtain more speedup. This paper shows how the parallelism type matching affects the time complexity of GATS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.794-815
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• 2023

The rapid development of mobile communication not only has made the industry gradually diversified, but also has enhanced the service quality requirements of users. In this regard, it is imperative to consider jointly network slicing and mobile edge computing. The former mainly ensures the requirements of varied vertical services preferably, and the latter solves the conflict between the user's own energy and harsh latency. At present, the integration of the two faces many challenges and need to carry out at different levels. The main target of the paper is to minimize the energy consumption of the system, and introduce a multi-slice joint task offloading and resource allocation scheme for massive multiple input multiple output enabled heterogeneous networks. The problem is formulated by collaborative optimizing offloading ratios, user association, transmission power and resource slicing, while being limited by the dissimilar latency and rate of multi-slice. To solve it, assign the optimal problem to two sub-problems of offloading decision and resource allocation, then solve them separately by exploiting the alternative optimization technique and Karush-Kuhn-Tucker conditions. Finally, a novel slices task offloading and resource allocation algorithm is proposed to get the offloading and resource allocation strategies. Numerous simulation results manifest that the proposed scheme has certain feasibility and effectiveness, and its performance is better than the other baseline scheme.

• 한국정보과학회논문지:시스템및이론
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• 제26권11호
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• pp.1333-1343
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• 1999

경성 실시간 시스템의 태스크들은 논리적으로 올바른 결과를 산출해야 하지만 또한 각자의 시간 제한 조건을 만족하여야 한다. 간격제한 스케줄링은 시간 제한 조건이 시간 간격 제한으로 주어지는 실시간 태스크들을 스케줄하기 위하여 도입되었다. 간격제한 스케줄링에서의 각 태스크들은 시간 간격 제한 조건을 갖는데, 이것은 태스크의 두 연속적인 수행의 종료시간에 대해 제한을 가한다. 다시 말해, 간격제한 스케줄링에서의 각 태스크 수행은 그 태스크의 직전 수행 완료 시간으로부터 발생하는 데드라인을 갖는다. 간격제한 태스크 스케줄링에 관한 많은 연구는 단순화 방법에 기초하고 있다. 그러나, 우리는 이 논문에서 단순화 방법을 사용하지 않고, 정적 우선순위 및 정적 분리 제한 정책을 채용한 새로운 간격제한 태스크 스케줄링 방법을 제안한다. 제안된 정적 할당 방법은 스케줄링 분석 및 구현을 매우 간단히 할 수 있으며, 또한 스케줄러의 실행시간 오버헤드를 줄일 수 있다.Abstract Tasks in hard real-time systems must not only be logically correct but also meet their timing constraints. The distance-constrained scheduling has been introduced to schedule real-time tasks whose timing constraints are characterized by temporal distance constraints. Each task in the distance-constrained scheduling has a temporal distance constraint which imposes restriction on the finishing times of two consecutive executions of the task. Thus, each execution of a task in the distance-constrained scheduling has a deadline relative to the finishing time of the previous execution of the task.Much work on the distance-constrained task scheduling has been based on the reduction technique. In this paper, we propose a new scheme for the distance-constrained task scheduling which does not use the reduction technique but adopts static priority and static separation constraint assignment policy. We show that our static assignment approach can simplify the scheduling analysis and its implementation, and can also reduce the run-time overhead of the scheduler.

• 정보처리학회논문지A
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• 제18A권6호
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• pp.255-264
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• 2011

센서 네트워크는 빛, 소리, 온도, 움직임 같은 물리적 데이터를 센서 노드에서 감지하고 측정하여 중앙으로 전달하고 처리하는 구조를 가진 네트워크이다. 센서 네트워크는 여러 분야에서 활용할 수 있는 기술이다. 센서 노드가 외부에서 채취한 데이터를 실시간으로 사용자에게 전달하는 것은 매우 중요하다. 센서 네트워크의 핵심은 센서 노드인 하드웨어 플랫폼과 노드에 들어가는 초소형 운영체제라고 할 수 있다. UC 버클리에서 개발된 TinyOS는 센서 노드에서 동작하는 운영체제 중 가장 많이 사용되고 있다. TinyOS는 Event-driven 방식이며 Component 기반의 센서 네트워크 운영체제이다. 기본적으로 비선점 방식의 스케줄러를 사용한다. 만약 급한 작업이 수행되어야 하는 시점에서 다른 태스크가 수행 중에 있다면 수행 중인 태스크가 완료할 때까지 기다려야 한다. 이러한 특성으로 인해 TinyOS에서 정해진 시간안에 자신의 작업을 끝낸다고 보장하기 어렵다. 최근 연구에서 TinyOS의 빠른 반응성을 위해 Priority Level Scheduler라는 선점 기능이 제안되었다. 이것은 제한적으로 5개의 우선순위를 만들어 높은 우선순위가 낮은 우선순위를 선점할 수 있게 한다. 여기서 본 논문은 TinyOS의 실시간성을 보장함과 더불어 사용자 태스크의 평균 응답시간을 줄이고자 Priority Level Scheduler에 실시간 스케줄러인 EDF(Earliest Deadline First)를 적용한 선점형 EDF 스케줄링 방식을 제안하고자 한다.

• 한국멀티미디어학회논문지
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• 제23권12호
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• pp.1476-1485
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• 2020

Segmenting OCT retinal images into layers is important to diagnose and understand the progression of retinal diseases or identify potential symptoms. The task of manually identifying these layers is a difficult task that requires a lot of time and effort even for medical professionals, and therefore, various studies are being conducted to automate this using deep learning technologies. In this paper, we use cGAN-based neural network to automatically segmenting OCT retinal images into seven terrain-type regions defined by six layer boundaries. The network is composed of a Segnet-based generator model and a discriminator model. We also proposed a dynamic programming algorithm for refining the outputs of the network. We performed experiments using public OCT image data set and compared its performance with the Segnet-only version of the network. The experimental results show that the cGAN-based network outperforms Segnet-only version.