141 research outputs found
On determinism versus nondeterminism for restarting automata
AbstractA restarting automaton processes a given word by executing a sequence of local simplifications until a simple word is obtained that the automaton then accepts. Such a computation is expressed as a sequence of cycles. A nondeterministic restarting automaton M is called correctness preserving, if, for each cycle uโขMcv, the string v belongs to the characteristic language LC(M) of M, if the string u does. Our first result states that for each type of restarting automaton Xโ{R,RW,RWW,RL,RLW,RLWW}, if M is a nondeterministic X-automaton that is correctness preserving, then there exists a deterministic X-automaton M1 such that the characteristic languages LC(M1) and LC(M) coincide. When a restarting automaton M executes a cycle that transforms a string from the language LC(M) into a string not belonging to LC(M), then this can be interpreted as an error of M. By counting the number of cycles it may take M to detect this error, we obtain a measure for the influence that errors have on computations. Accordingly, this measure is called error detection distance. It turns out, however, that an X-automaton with bounded error detection distance is equivalent to a correctness preserving X-automaton, and therewith to a deterministic X-automaton. This means that nondeterminism increases the expressive power of X-automata only in combination with an unbounded error detection distance
26. Theorietag Automaten und Formale Sprachen 23. Jahrestagung Logik in der Informatik: Tagungsband
Der Theorietag ist die Jahrestagung der Fachgruppe Automaten und Formale Sprachen der Gesellschaft fรผr Informatik und fand erstmals 1991 in Magdeburg statt. Seit dem Jahr 1996 wird der Theorietag von einem eintรคgigen Workshop mit eingeladenen Vortrรคgen begleitet. Die Jahrestagung der Fachgruppe Logik in der Informatik der Gesellschaft fรผr Informatik fand erstmals 1993 in Leipzig statt. Im Laufe beider Jahrestagungen finden auch die jรคhrliche Fachgruppensitzungen statt. In diesem Jahr wird der Theorietag der Fachgruppe Automaten und Formale Sprachen erstmalig zusammen mit der Jahrestagung der Fachgruppe Logik in der Informatik abgehalten. Organisiert wurde die gemeinsame Veranstaltung von der Arbeitsgruppe Zuverlรคssige Systeme des Instituts fรผr Informatik an der Christian-Albrechts-Universitรคt Kiel vom 4. bis 7. Oktober im Tagungshotel Tannenfelde bei Neumรผnster. Wรคhrend des Treโตens wird ein Workshop fรผr alle Interessierten statt finden. In Tannenfelde werden โข Christoph Lรถding (Aachen) โข Tomรกs Masopust (Dresden) โข Henning Schnoor (Kiel) โข Nicole Schweikardt (Berlin) โข Georg Zetzsche (Paris) eingeladene Vortrรคge zu ihrer aktuellen Arbeit halten. Darรผber hinaus werden 26 Vortrรคge von Teilnehmern und Teilnehmerinnen gehalten, 17 auf dem Theorietag Automaten und formale Sprachen und neun auf der Jahrestagung Logik in der Informatik. Der vorliegende Band enthรคlt Kurzfassungen aller Beitrรคge. Wir danken der Gesellschaft fรผr Informatik, der Christian-Albrechts-Universitรคt zu Kiel und dem Tagungshotel Tannenfelde fรผr die Unterstรผtzung dieses Theorietags. Ein besonderer Dank geht an das Organisationsteam: Maike Bradler, Philipp Sieweck, Joel Day. Kiel, Oktober 2016 Florin Manea, Dirk Nowotka und Thomas Wilk
Abstract geometrical computation 7: geometrical accumulations and computably enumerable real numbers
For differential equations with r parameters, 2r+1 experiments are enough for identification
Given a set of differential equations whose description involves unknown
parameters, such as reaction constants in chemical kinetics, and supposing that
one may at any time measure the values of some of the variables and possibly
apply external inputs to help excite the system, how many experiments are
sufficient in order to obtain all the information that is potentially available
about the parameters? This paper shows that the best possible answer (assuming
exact measurements) is 2r+1 experiments, where r is the number of parameters.Comment: This is a minor revision of the previously submitted report; a couple
of typos have been fixed, and some comments and two new references have been
added. Please see http://www.math.rutgers.edu/~sontag for related wor
๊ฐ์ธํ ๋ํํ ์์ ๋ถํ ์๊ณ ๋ฆฌ์ฆ์ ์ํ ์๋ ์ ๋ณด ํ์ฅ ๊ธฐ๋ฒ์ ๋ํ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2021. 2. ์ด๊ฒฝ๋ฌด.Segmentation of an area corresponding to a desired object in an image is essential
to computer vision problems. This is because most algorithms are performed in
semantic units when interpreting or analyzing images. However, segmenting the
desired object from a given image is an ambiguous issue. The target object varies
depending on user and purpose. To solve this problem, an interactive segmentation
technique has been proposed. In this approach, segmentation was performed in the
desired direction according to interaction with the user. In this case, seed information
provided by the user plays an important role. If the seed provided by a user contain
abundant information, the accuracy of segmentation increases. However, providing
rich seed information places much burden on the users. Therefore, the main goal of
the present study was to obtain satisfactory segmentation results using simple seed
information.
We primarily focused on converting the provided sparse seed information to a rich
state so that accurate segmentation results can be derived. To this end, a minimum
user input was taken and enriched it through various seed enrichment techniques.
A total of three interactive segmentation techniques was proposed based on: (1)
Seed Expansion, (2) Seed Generation, (3) Seed Attention. Our seed enriching type
comprised expansion of area around a seed, generation of new seed in a new position,
and attention to semantic information.
First, in seed expansion, we expanded the scope of the seed. We integrated reliable
pixels around the initial seed into the seed set through an expansion step
composed of two stages. Through the extended seed covering a wider area than the
initial seed, the seed's scarcity and imbalance problems was resolved. Next, in seed
generation, we created a seed at a new point, but not around the seed. We trained
the system by imitating the user behavior through providing a new seed point in the
erroneous region. By learning the user's intention, our model could e ciently create
a new seed point. The generated seed helped segmentation and could be used as additional
information for weakly supervised learning. Finally, through seed attention,
we put semantic information in the seed. Unlike the previous models, we integrated
both the segmentation process and seed enrichment process. We reinforced the seed
information by adding semantic information to the seed instead of spatial expansion.
The seed information was enriched through mutual attention with feature maps
generated during the segmentation process.
The proposed models show superiority compared to the existing techniques
through various experiments. To note, even with sparse seed information, our proposed
seed enrichment technique gave by far more accurate segmentation results
than the other existing methods.์์์์ ์ํ๋ ๋ฌผ์ฒด ์์ญ์ ์๋ผ๋ด๋ ๊ฒ์ ์ปดํจํฐ ๋น์ ๋ฌธ์ ์์ ํ์์ ์ธ ์์์ด๋ค. ์์์ ํด์ํ๊ฑฐ๋ ๋ถ์ํ ๋, ๋๋ถ๋ถ์ ์๊ณ ๋ฆฌ์ฆ๋ค์ด ์๋ฏธ๋ก ์ ์ธ ๋จ์ ๊ธฐ๋ฐ์ผ๋ก ๋์ํ๊ธฐ ๋๋ฌธ์ด๋ค. ๊ทธ๋ฌ๋ ์์์์ ๋ฌผ์ฒด ์์ญ์ ๋ถํ ํ๋ ๊ฒ์ ๋ชจํธํ ๋ฌธ์ ์ด๋ค. ์ฌ์ฉ์์ ๋ชฉ์ ์ ๋ฐ๋ผ ์ํ๋ ๋ฌผ์ฒด ์์ญ์ด ๋ฌ๋ผ์ง๊ธฐ ๋๋ฌธ์ด๋ค. ์ด๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ์ฌ์ฉ์์์ ๊ต๋ฅ๋ฅผ ํตํด ์ํ๋ ๋ฐฉํฅ์ผ๋ก ์์ ๋ถํ ์ ์งํํ๋ ๋ํํ ์์ ๋ถํ ๊ธฐ๋ฒ์ด ์ฌ์ฉ๋๋ค. ์ฌ๊ธฐ์ ์ฌ์ฉ์๊ฐ ์ ๊ณตํ๋ ์๋ ์ ๋ณด๊ฐ ์ค์ํ ์ญํ ์ ํ๋ค. ์ฌ์ฉ์์ ์๋๋ฅผ ๋ด๊ณ ์๋ ์๋ ์ ๋ณด๊ฐ ์ ํํ ์๋ก ์์ ๋ถํ ์ ์ ํ๋๋ ์ฆ๊ฐํ๊ฒ ๋๋ค. ๊ทธ๋ฌ๋ ํ๋ถํ ์๋ ์ ๋ณด๋ฅผ ์ ๊ณตํ๋ ๊ฒ์ ์ฌ์ฉ์์๊ฒ ๋ง์ ๋ถ๋ด์ ์ฃผ๊ฒ ๋๋ค. ๊ทธ๋ฌ๋ฏ๋ก ๊ฐ๋จํ ์๋ ์ ๋ณด๋ฅผ ์ฌ์ฉํ์ฌ ๋ง์กฑํ ๋งํ ๋ถํ ๊ฒฐ๊ณผ๋ฅผ ์ป๋ ๊ฒ์ด ์ฃผ์ ๋ชฉ์ ์ด ๋๋ค.
์ฐ๋ฆฌ๋ ์ ๊ณต๋ ํฌ์ํ ์๋ ์ ๋ณด๋ฅผ ๋ณํํ๋ ์์
์ ์ด์ ์ ๋์๋ค. ๋ง์ฝ ์๋ ์ ๋ณด๊ฐ ํ๋ถํ๊ฒ ๋ณํ๋๋ค๋ฉด ์ ํํ ์์ ๋ถํ ๊ฒฐ๊ณผ๋ฅผ ์ป์ ์ ์๊ธฐ ๋๋ฌธ์ด๋ค. ๊ทธ๋ฌ๋ฏ๋ก ๋ณธ ํ์ ๋
ผ๋ฌธ์์๋ ์๋ ์ ๋ณด๋ฅผ ํ๋ถํ๊ฒ ํ๋ ๊ธฐ๋ฒ๋ค์ ์ ์ํ๋ค. ์ต์ํ์ ์ฌ์ฉ์ ์
๋ ฅ์ ๊ฐ์ ํ๊ณ ์ด๋ฅผ ๋ค์ํ ์๋ ํ์ฅ ๊ธฐ๋ฒ์ ํตํด ๋ณํํ๋ค. ์ฐ๋ฆฌ๋ ์๋ ํ๋, ์๋ ์์ฑ, ์๋ ์ฃผ์ ์ง์ค์ ๊ธฐ๋ฐํ ์ด ์ธ ๊ฐ์ง์ ๋ํํ ์์ ๋ถํ ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ๊ฐ๊ฐ ์๋ ์ฃผ๋ณ์ผ๋ก์ ์์ญ ํ๋, ์๋ก์ด ์ง์ ์ ์๋ ์์ฑ, ์๋ฏธ๋ก ์ ์ ๋ณด์ ์ฃผ๋ชฉํ๋ ํํ์ ์๋ ํ์ฅ ๊ธฐ๋ฒ์ ์ฌ์ฉํ๋ค.
๋จผ์ ์๋ ํ๋์ ๊ธฐ๋ฐํ ๊ธฐ๋ฒ์์ ์ฐ๋ฆฌ๋ ์๋์ ์์ญ ํ์ฅ์ ๋ชฉํ๋ก ํ๋ค. ๋ ๋จ๊ณ๋ก ๊ตฌ์ฑ๋ ํ๋ ๊ณผ์ ์ ํตํด ์ฒ์ ์๋ ์ฃผ๋ณ์ ๋น์ทํ ํฝ์
๋ค์ ์๋ ์์ญ์ผ๋ก ํธ์
ํ๋ค. ์ด๋ ๊ฒ ํ์ฅ๋ ์๋๋ฅผ ์ฌ์ฉํจ์ผ๋ก์จ ์๋์ ํฌ์ํจ๊ณผ ๋ถ๊ท ํ์ผ๋ก ์ธํ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ ์ ์๋ค. ๋ค์์ผ๋ก ์๋ ์์ฑ์ ๊ธฐ๋ฐํ ๊ธฐ๋ฒ์์ ์ฐ๋ฆฌ๋ ์๋ ์ฃผ๋ณ์ด ์๋ ์๋ก์ด ์ง์ ์ ์๋๋ฅผ ์์ฑํ๋ค. ์ฐ๋ฆฌ๋ ์ค์ฐจ๊ฐ ๋ฐ์ํ ์์ญ์ ์ฌ์ฉ์๊ฐ ์๋ก์ด ์๋๋ฅผ ์ ๊ณตํ๋ ๋์์ ๋ชจ๋ฐฉํ์ฌ ์์คํ
์ ํ์ตํ์๋ค. ์ฌ์ฉ์์ ์๋๋ฅผ ํ์ตํจ์ผ๋ก์จ ํจ๊ณผ์ ์ผ๋ก ์๋๋ฅผ ์์ฑํ ์ ์๋ค. ์์ฑ๋ ์๋๋ ์์ ๋ถํ ์ ์ ํ๋๋ฅผ ๋์ผ ๋ฟ๋ง ์๋๋ผ ์ฝ์ง๋ํ์ต์ ์ํ ๋ฐ์ดํฐ๋ก์จ ํ์ฉ๋ ์ ์๋ค. ๋ง์ง๋ง์ผ๋ก ์๋ ์ฃผ์ ์ง์ค์ ํ์ฉํ ๊ธฐ๋ฒ์์ ์ฐ๋ฆฌ๋ ์๋ฏธ๋ก ์ ์ ๋ณด๋ฅผ ์๋์ ๋ด๋๋ค. ๊ธฐ์กด์ ์ ์ํ ๊ธฐ๋ฒ๋ค๊ณผ ๋ฌ๋ฆฌ ์์ ๋ถํ ๋์๊ณผ ์๋ ํ์ฅ ๋์์ด ํตํฉ๋ ๋ชจ๋ธ์ ์ ์ํ๋ค. ์๋ ์ ๋ณด๋ ์์ ๋ถํ ๋คํธ์ํฌ์ ํน์ง๋งต๊ณผ ์ํธ ๊ต๋ฅํ๋ฉฐ ๊ทธ ์ ๋ณด๊ฐ ํ๋ถํด์ง๋ค.
์ ์ํ ๋ชจ๋ธ๋ค์ ๋ค์ํ ์คํ์ ํตํด ๊ธฐ์กด ๊ธฐ๋ฒ ๋๋น ์ฐ์ํ ์ฑ๋ฅ์ ๊ธฐ๋กํ์๋ค. ํนํ ์๋๊ฐ ๋ถ์กฑํ ์ํฉ์์ ์๋ ํ์ฅ ๊ธฐ๋ฒ๋ค์ ํ๋ฅญํ ๋ํํ ์์ ๋ถํ ์ฑ๋ฅ์ ๋ณด์๋ค.1 Introduction 1
1.1 Previous Works 2
1.2 Proposed Methods 4
2 Interactive Segmentation with Seed Expansion 9
2.1 Introduction 9
2.2 Proposed Method 12
2.2.1 Background 13
2.2.2 Pyramidal RWR 16
2.2.3 Seed Expansion 19
2.2.4 Re nement with Global Information 24
2.3 Experiments 27
2.3.1 Dataset 27
2.3.2 Implement Details 28
2.3.3 Performance 29
2.3.4 Contribution of Each Part 30
2.3.5 Seed Consistency 31
2.3.6 Running Time 33
2.4 Summary 34
3 Interactive Segmentation with Seed Generation 37
3.1 Introduction 37
3.2 Related Works 40
3.3 Proposed Method 41
3.3.1 System Overview 41
3.3.2 Markov Decision Process 42
3.3.3 Deep Q-Network 46
3.3.4 Model Architecture 47
3.4 Experiments 48
3.4.1 Implement Details 48
3.4.2 Performance 49
3.4.3 Ablation Study 53
3.4.4 Other Datasets 55
3.5 Summary 58
4 Interactive Segmentation with Seed Attention 61
4.1 Introduction 61
4.2 Related Works 64
4.3 Proposed Method 65
4.3.1 Interactive Segmentation Network 65
4.3.2 Bi-directional Seed Attention Module 67
4.4 Experiments 70
4.4.1 Datasets 70
4.4.2 Metrics 70
4.4.3 Implement Details 71
4.4.4 Performance 71
4.4.5 Ablation Study 76
4.4.6 Seed enrichment methods 79
4.5 Summary 82
5 Conclusions 87
5.1 Summary 89
Bibliography 90
๊ตญ๋ฌธ์ด๋ก 103Docto
Safety and security of cyber-physical systems
The number of embedded controllers in charge of physical systems has rapidly increased over the past years. Embedded controllers are present in every aspect of our lives, from our homes to our vehicles and factories. The complexity of these systems is also more than ever. These systems are expected to deliver many features and high performance without trading off in robustness and assurance. As systems increase in complexity, however, the cost of formally verifying their correctness and eliminating security vulnerabilities can quickly explode. On top of the unintentional bugs and problems, malicious attacks on cyber-physical systems (CPS) can also lead to adverse outcomes on physical plants. Some of the recent attacks on CPS are focused on causing physical damage to the plants or the environment. Such intruders make their way into the system using cyber exploits but then initiate actions that can destabilize and even damage the underlying (physical) systems.
Given the reality mentioned above and the reliability standards of the industry, there is a need to embrace new CPS design paradigms where faults and security vulnerabilities are the norms rather than an anomaly. Such imperfections must be assumed to exist in every system and component unless it is formally verified and scanned. Faults and vulnerabilities should be safely handled and the CPS must be able to recover from them at run-time. Our goal in this work is to introduce and investigate a few designs compatible with this paradigm. The architectures and techniques proposed in this dissertation do not rely on the testing and complete system verification. Instead, they enforce safety at the highest level of the system and extend guaranteed safety from a few certified components to the entire system. These solutions are carefully curated to utilize unverified components and provide guaranteed performance
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